Triple

T15002893
Position Surface form Disambiguated ID Type / Status
Subject Divinae Institutiones E374134 entity
Predicate book2Title P33185 FINISHED
Object De origine erroris (On the Origin of Error)
De origine erroris (On the Origin of Error) is the second book of Lactantius’s early Christian apologetic work *Divinae Institutiones*, examining the roots and nature of human religious and philosophical error.
E1131979 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: De origine erroris (On the Origin of Error) | Statement: [Divinae Institutiones, book2Title, De origine erroris (On the Origin of Error)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: De origine erroris (On the Origin of Error)
Context triple: [Divinae Institutiones, book2Title, De origine erroris (On the Origin of Error)]
  • A. Gaussian law of error
    The Gaussian law of error is a fundamental statistical principle stating that measurement errors tend to follow a normal (bell-shaped) distribution, forming the basis of much of probability theory and statistical inference.
  • B. Laplace law of error
    The Laplace law of error is a probability distribution characterized by a sharp peak at the mean and heavier tails than the normal distribution, historically used to model the magnitude of observational errors.
  • C. Syllabus of Errors
    Syllabus of Errors is a 19th-century papal document that catalogues and condemns a series of modern philosophical, political, and religious doctrines deemed incompatible with Catholic teaching.
  • D. The Logic of Chance
    The Logic of Chance is an influential 1866 book by John Venn that helped establish the frequency interpretation of probability and advanced the philosophical foundations of statistical reasoning.
  • E. Enumeration of the Sciences
    Enumeration of the Sciences is a foundational philosophical treatise by Al-Farabi that systematically classifies and analyzes the various branches of knowledge in the Islamic intellectual tradition.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: De origine erroris (On the Origin of Error)
Triple: [Divinae Institutiones, book2Title, De origine erroris (On the Origin of Error)]
Generated description
De origine erroris (On the Origin of Error) is the second book of Lactantius’s early Christian apologetic work *Divinae Institutiones*, examining the roots and nature of human religious and philosophical error.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: De origine erroris (On the Origin of Error)
Target entity description: De origine erroris (On the Origin of Error) is the second book of Lactantius’s early Christian apologetic work *Divinae Institutiones*, examining the roots and nature of human religious and philosophical error.
  • A. Gaussian law of error
    The Gaussian law of error is a fundamental statistical principle stating that measurement errors tend to follow a normal (bell-shaped) distribution, forming the basis of much of probability theory and statistical inference.
  • B. Laplace law of error
    The Laplace law of error is a probability distribution characterized by a sharp peak at the mean and heavier tails than the normal distribution, historically used to model the magnitude of observational errors.
  • C. Syllabus of Errors
    Syllabus of Errors is a 19th-century papal document that catalogues and condemns a series of modern philosophical, political, and religious doctrines deemed incompatible with Catholic teaching.
  • D. The Logic of Chance
    The Logic of Chance is an influential 1866 book by John Venn that helped establish the frequency interpretation of probability and advanced the philosophical foundations of statistical reasoning.
  • E. Enumeration of the Sciences
    Enumeration of the Sciences is a foundational philosophical treatise by Al-Farabi that systematically classifies and analyzes the various branches of knowledge in the Islamic intellectual tradition.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d85ccc84388190aa151e5173370c8d completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded7312ae48190bdaf91ecced6657e completed April 15, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe96a04eec8190b347bf3637aba0bc completed May 9, 2026, 2:06 a.m.
NEDg Description generation batch_69fe98e182708190a013511c32d33315 completed May 9, 2026, 2:16 a.m.
NED2 Entity disambiguation (via description) batch_69fe9a48e85081909d70d8f44e3a54d7 completed May 9, 2026, 2:22 a.m.
Created at: April 10, 2026, 2:54 a.m.