Triple

T180970
Position Surface form Disambiguated ID Type / Status
Subject CrossCountry E3874 entity
Predicate operatesServiceTo P6304 FINISHED
Object Reading
Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
 E22663 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: Reading | Statement: [CrossCountry, operatesServiceTo, Reading]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Reading
Context triple: [CrossCountry, operatesServiceTo, Reading]
  • A. Writings
    Writings is the third major section of the Hebrew Bible, comprising a diverse collection of poetic, wisdom, and historical books such as Psalms, Proverbs, and Job.
  • B. Pan Books
    Pan Books is a British publishing imprint known for producing popular fiction and classic titles, including major science fiction works.
  • C. Times Books
    Times Books is a publishing imprint known for producing nonfiction works, particularly in the fields of history, politics, and current affairs.
  • D. REC
    REC is the standard abbreviation used by the World Wide Web Consortium (W3C) to denote a finalized, stable web standard known as a W3C Recommendation.
  • E. Stories That Matter
    Stories That Matter is the guiding motto of the Peabody Awards, emphasizing their focus on honoring impactful and socially significant storytelling in media.
  • 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: Reading
Triple: [CrossCountry, operatesServiceTo, Reading]
Generated description
Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Reading
Target entity description: Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
  • A. Writings
    Writings is the third major section of the Hebrew Bible, comprising a diverse collection of poetic, wisdom, and historical books such as Psalms, Proverbs, and Job.
  • B. Pan Books
    Pan Books is a British publishing imprint known for producing popular fiction and classic titles, including major science fiction works.
  • C. Times Books
    Times Books is a publishing imprint known for producing nonfiction works, particularly in the fields of history, politics, and current affairs.
  • D. REC
    REC is the standard abbreviation used by the World Wide Web Consortium (W3C) to denote a finalized, stable web standard known as a W3C Recommendation.
  • E. Stories That Matter
    Stories That Matter is the guiding motto of the Peabody Awards, emphasizing their focus on honoring impactful and socially significant storytelling in media.
  • 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_69a25497e2f08190a040f8c6e1842643 completed Feb. 28, 2026, 2:36 a.m.
NER Named-entity recognition batch_69a25bafd5808190a0a0cb2b21ce007f completed Feb. 28, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2f0b71080819086362f6036b41162 completed Feb. 28, 2026, 1:42 p.m.
NEDg Description generation batch_69a2f12f181c8190bae3098a2928feff completed Feb. 28, 2026, 1:44 p.m.
NED2 Entity disambiguation (via description) batch_69a2f3771fcc8190b8486be33f695524 completed Feb. 28, 2026, 1:53 p.m.
Created at: Feb. 28, 2026, 2:40 a.m.