Triple
T2098549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pierre Bayle |
E37039
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Pierre |
E3672
|
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: Pierre | Statement: [Pierre Bayle, givenName, Pierre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pierre Context triple: [Pierre Bayle, givenName, Pierre]
-
A.
Pierre
chosen
Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
-
B.
Étienne
Étienne is the given first name of the French Symbolist poet Stéphane Mallarmé.
-
C.
René
René is a French given name commonly used for males and historically associated with several notable figures in politics, arts, and philosophy.
-
D.
Jacques
Jacques is the French form of the given name James, commonly used in French-speaking countries.
-
E.
Eugène
Eugène is a masculine given name of French origin, derived from the Greek "Eugenios," meaning "well-born" or "noble."
- 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_69a8861828948190924aa30c08806b3a |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba9de75c81909770409b5ae62c24 |
completed | March 7, 2026, 5:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afe86f11108190a1987c1cee133d56 |
completed | March 10, 2026, 9:46 a.m. |
Created at: March 4, 2026, 7:43 p.m.