Triple
T37198102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tosca |
E921647
|
entity |
| Predicate | operaNumberInPucciniCareer |
P103987
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Tosca, operaNumberInPucciniCareer, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operaNumberInPucciniCareer Context triple: [Tosca, operaNumberInPucciniCareer, 5]
-
A.
operaNumberInVerdiOeuvre
Indicates the ordinal position of an opera within the complete body of operatic works composed by Verdi.
-
B.
operaNumberInComposerOutput
chosen
Indicates the ordinal position or catalog number assigned to an opera within the complete body of works by a specific composer.
-
C.
numberOfOperas
Indicates the total count of operas associated with a given entity (such as a person, organization, or catalog entry).
-
D.
operaNumberInMozartsOutput
Indicates the ordinal position or catalog number assigned to an opera within the complete body of Mozart’s operatic works.
-
E.
estimatedNumberOfOperas
Indicates the approximate count of operas associated with an entity, rather than an exact, verified number.
- 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_69f76ea313a08190a54404cd1e47da90 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a02145017b08190bd424cc647c08d3b |
completed | May 11, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_6a021338e2b08190980c489ee44c18b3 |
completed | May 11, 2026, 5:34 p.m. |
Created at: May 3, 2026, 4:15 p.m.