Triple
T5891007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darwinsys Java Cookbook examples |
E130986
|
entity |
| Predicate | maintainer |
P2962
|
FINISHED |
| Object | Ian Darwin |
E23092
|
NE FINISHED |
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: Ian Darwin | Statement: [Darwinsys Java Cookbook examples, maintainer, Ian Darwin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ian Darwin Context triple: [Darwinsys Java Cookbook examples, maintainer, Ian Darwin]
-
A.
Ian Darwin
chosen
Ian Darwin is a software developer and author best known for his contributions to Unix, Java, and open source programming resources.
-
B.
John Darwin
John Darwin is a British former teacher and prison officer who infamously faked his own death in a canoeing accident in 2002 as part of an insurance fraud scheme.
-
C.
John Darwin
John Darwin is a British historian and academic known for his influential work on the history of empires, particularly the British Empire and global imperialism.
-
D.
Carl Zimmer
Carl Zimmer is an American science writer and journalist renowned for his accessible and insightful books and articles on biology, evolution, and genetics.
-
E.
Michael Darwin
Michael Darwin is an American writer and researcher best known for his influential work and advocacy in the field of cryonics.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c00857439c819095950754176aa58a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c036b228508190b050acf51860a5c2 |
completed | March 22, 2026, 6:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c108039acc8190a5ce23412f1a359c |
completed | March 23, 2026, 9:29 a.m. |
Created at: March 22, 2026, 3:58 p.m.