Triple
T3081157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | André Citroën |
E64258
|
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 Citroën, givenName, André]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: André Context triple: [André Citroën, 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 Pascal
André Pascal was a notable French figure, likely a politician or public servant, after whom Rue André Pascal in Paris is named.
-
C.
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."
-
D.
Michel
Michel is a French given name commonly used for males, equivalent to "Michael" in English.
-
E.
Benoît
Benoît is the French form of the given name Benedict, commonly used in French-speaking countries.
- 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_69ad857bb4c88190a4cf27893fcabed8 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada1aaf6d48190af4f9106965589b0 |
completed | March 8, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e817b0d881909365a6bf5eb5104b |
completed | March 12, 2026, 4:21 p.m. |
Created at: March 8, 2026, 3:03 p.m.