Triple
T32204023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L’Africaine |
E822620
|
entity |
| Predicate | operaHouseAssociation |
P92128
|
FINISHED |
| Object | Paris Opéra repertoire |
—
|
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: Paris Opéra repertoire | Statement: [L’Africaine, operaHouseAssociation, Paris Opéra repertoire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operaHouseAssociation Context triple: [L’Africaine, operaHouseAssociation, Paris Opéra repertoire]
-
A.
hasOperaHouseAssociation
chosen
Indicates a relationship where an entity is connected or related to an opera house, such as through location, ownership, operation, affiliation, or significant involvement.
-
B.
operaHouseType
Indicates the specific kind or classification of an opera house associated with an entity.
-
C.
hasOperaHouse
Indicates that one entity possesses, contains, or is the location of an opera house.
-
D.
operaFestivalVenue
Indicates that a venue is the location where an opera festival is held.
-
E.
largestOperaCompanyIn
Indicates that an entity is the largest opera company (by some measure such as size, budget, or capacity) within a specified geographic or organizational area.
- F. None of above.
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_69f349093174819086e633c190a51aa8 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fffbb5d0188190b6d168de8626ff68 |
completed | May 10, 2026, 3:29 a.m. |
| PD | Predicate disambiguation | batch_69fffa3bc1208190a277961385a4789f |
completed | May 10, 2026, 3:23 a.m. |
Created at: May 1, 2026, 12:36 a.m.