Triple
T5557001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wooster stories |
E145667
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Anatole |
E217999
|
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: Anatole | Statement: [Wooster stories, featuresCharacter, Anatole]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anatole Context triple: [Wooster stories, featuresCharacter, Anatole]
-
A.
Anatole
chosen
Anatole is the famously temperamental and gifted French chef employed by Aunt Dahlia in P. G. Wodehouse’s Jeeves and Wooster stories.
-
B.
Pierre
Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
-
C.
Antoine
Antoine is the given name of Antoine de la Mothe Cadillac, the French explorer and founder of Detroit.
-
D.
Eugène
Eugène is a masculine given name of French origin, derived from the Greek "Eugenios," meaning "well-born" or "noble."
-
E.
Georges
Georges is a masculine given name of Greek origin, commonly used in French-speaking countries and derived from the name George, meaning "farmer" or "earthworker."
- 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_69c008fcaf788190bafa02a1917ee73b |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01ffe12cc81908186f28ace0f4d82 |
completed | March 22, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07d7658a481909e9e9b29df2b148e |
completed | March 22, 2026, 11:38 p.m. |
Created at: March 22, 2026, 3:36 p.m.