Triple
T13277246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | André Antoine |
E316222
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | André |
E24111
|
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: André | Statement: [André Antoine, givenName, André]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: André Context triple: [André Antoine, givenName, André]
-
A.
André
chosen
André is a given name of French origin commonly used in various languages as a form of "Andrew."
-
B.
André David
André David was one of the discoverers of the prehistoric painted cave of Pech Merle in southwestern France.
-
C.
André Pascal
André Pascal was a notable French figure, likely a politician or public servant, after whom Rue André Pascal in Paris is named.
-
D.
Hervé
Hervé is a French given name, often considered a variant of the English name Harvey, and is commonly used for males in French-speaking regions.
-
E.
André Daina
André Daina is a Swiss former football referee best known for officiating major European club competitions in the 1980s.
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99042f56c819082440c89c0adc442 |
completed | April 11, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7266665488190a4e2e411bafada85 |
completed | May 3, 2026, 10:41 a.m. |
Created at: April 9, 2026, 9:26 p.m.