Triple
T34120298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Ménégoz |
E875105
|
entity |
| Predicate | roleInParisTexas |
P202791
|
FINISHED |
| Object | producer |
—
|
LITERAL 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: producer | Statement: [Margaret Ménégoz, roleInParisTexas, producer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInParisTexas Context triple: [Margaret Ménégoz, roleInParisTexas, producer]
-
A.
roleAtHouston
Indicates that an entity holds or held a specific role or position at an organization, institution, or context associated with Houston.
-
B.
roleInPlanoReal
Indicates that an entity holds or held a specific role or function within the Plano Real economic plan or initiative.
-
C.
roleInBordeaux
Indicates that an entity holds or has held a specific role, function, or position within the context of Bordeaux.
-
D.
hasCityRole
Indicates that an entity holds or is assigned a specific role, function, or status within a particular city.
-
E.
homeCityRole
Indicates that an entity holds or held a specific role, position, or function associated with their home city.
- F. None of above. chosen
Provenance (4 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_69f349a9271c81909576994c9ef7b179 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a00bb3a6f888190b3ecd0fbc9af9b4a |
completed | May 10, 2026, 5:07 p.m. |
| PD | Predicate disambiguation | batch_6a00b902dbf881909e098ff102b7ea7e |
completed | May 10, 2026, 4:57 p.m. |
| PDg | Predicate description generation | batch_6a00bb39c9c88190b82c8a8fb6489a61 |
completed | May 10, 2026, 5:07 p.m. |
Created at: May 1, 2026, 1:53 a.m.