Triple
T17522215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PEP 503 |
E426701
|
entity |
| Predicate | createdBy |
P806
|
FINISHED |
| Object |
Daniel Holth
Daniel Holth is a Python developer best known for his work on Python packaging standards, including authoring PEP 503.
|
E1277686
|
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: Daniel Holth | Statement: [PEP 503, createdBy, Daniel Holth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Holth Context triple: [PEP 503, createdBy, Daniel Holth]
-
A.
Daniel Heins
Daniel Heins is the anglicized name of Daniel Heinsius, a prominent Dutch classical scholar and poet of the early 17th century.
-
B.
Daniel Holley
Daniel Holley is a central character in Jack Higgins’ thriller novel "A Devil Is Waiting," depicted as a skilled and resourceful operative involved in high-stakes international intrigue.
-
C.
Michael Holzer
Michael Holzer is an architect best known as one of the founders of the avant-garde Austrian architecture firm Coop Himmelb(l)au.
-
D.
David Hollaz
David Hollaz was a prominent late 17th- and early 18th-century German Lutheran theologian known for his systematic works that became standard texts in Lutheran dogmatics.
-
E.
Daniel Roher
Daniel Roher is a Canadian documentary filmmaker best known for directing the Oscar-winning political documentary "Navalny."
- 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: Daniel Holth Triple: [PEP 503, createdBy, Daniel Holth]
Generated description
Daniel Holth is a Python developer best known for his work on Python packaging standards, including authoring PEP 503.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Holth Target entity description: Daniel Holth is a Python developer best known for his work on Python packaging standards, including authoring PEP 503.
-
A.
Daniel Heins
Daniel Heins is the anglicized name of Daniel Heinsius, a prominent Dutch classical scholar and poet of the early 17th century.
-
B.
Daniel Holley
Daniel Holley is a central character in Jack Higgins’ thriller novel "A Devil Is Waiting," depicted as a skilled and resourceful operative involved in high-stakes international intrigue.
-
C.
Michael Holzer
Michael Holzer is an architect best known as one of the founders of the avant-garde Austrian architecture firm Coop Himmelb(l)au.
-
D.
David Hollaz
David Hollaz was a prominent late 17th- and early 18th-century German Lutheran theologian known for his systematic works that became standard texts in Lutheran dogmatics.
-
E.
Daniel Roher
Daniel Roher is a Canadian documentary filmmaker best known for directing the Oscar-winning political documentary "Navalny."
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d2f79881909556894728e255ab |
completed | April 19, 2026, 3:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01e80c38248190bf350df5211f0518 |
completed | May 11, 2026, 2:30 p.m. |
| NEDg | Description generation | batch_6a01ecfb2ff4819082f67ab1f2ce8885 |
completed | May 11, 2026, 2:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01ee0c03e881908aaaa3bd0f596387 |
completed | May 11, 2026, 2:56 p.m. |
Created at: April 10, 2026, 5:49 a.m.