Triple
T17522263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PEP 508 |
E426702
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Nick Coghlan |
—
|
NE NERFINISHED |
How this triple was built (2 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: Nick Coghlan | Statement: [PEP 508, author, Nick Coghlan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nick Coghlan Context triple: [PEP 508, author, Nick Coghlan]
-
A.
Nick Coghlan
chosen
Nick Coghlan is a prominent Python core developer and software engineer known for his influential work on Python’s governance, documentation, and language design.
-
B.
Ben Finney
Ben Finney was an anthropologist and pioneer of experimental archaeology best known for reviving traditional Polynesian navigation and co-founding the Polynesian Voyaging Society.
-
C.
Robert Kern
Robert Kern was an American film editor active during Hollywood’s classic studio era, known for his work on numerous prominent MGM productions.
-
D.
Chris Angelico
Chris Angelico is a Python developer and community contributor known for his involvement in Python Enhancement Proposals, including co-authoring PEP 572.
-
E.
David Goodger
David Goodger is a software developer and technical writer best known for creating the reStructuredText markup language and contributing to the Python community.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e452d40ee08190b79d8e3d7f1b1272 |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.