Triple
T7025015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | André De Toth |
E162921
|
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 De Toth, givenName, André]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: André Context triple: [André De Toth, 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.
Arnaud
Arnaud is a small commune located in Haiti’s Nippes Department.
-
E.
Michel
Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1fb8f0c8190b15dd7ce7ab6a8f2 |
completed | March 27, 2026, 8 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79439f28481909c028f4f86062b0f |
completed | March 28, 2026, 8:41 a.m. |
Created at: March 27, 2026, 2:35 p.m.