Triple
T29789013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Notre Dame de Paris (Korean cast version) |
E756345
|
entity |
| Predicate | usesScoreBy |
P182248
|
FINISHED |
| Object | Riccardo Cocciante |
—
|
NE NERFINISHED |
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: Riccardo Cocciante | Statement: [Notre Dame de Paris (Korean cast version), usesScoreBy, Riccardo Cocciante]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesScoreBy Context triple: [Notre Dame de Paris (Korean cast version), usesScoreBy, Riccardo Cocciante]
-
A.
usesScore
Indicates that one entity evaluates, measures, or makes decisions about another entity by applying a numerical or categorical score.
-
B.
scoreUsedFor
Indicates that a particular score or rating is used for a specific purpose, decision, or downstream process.
-
C.
usesCompositeScore
Indicates that an entity bases its evaluation, decision, or outcome on a combined score derived from multiple underlying metrics or factors.
-
D.
isScoreFor
Indicates that one value represents the score or result associated with a particular entity, event, or performance.
-
E.
usesExistingScore
Indicates that an entity relies on or applies a previously calculated or established score rather than generating a new one.
- 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_69f22451fb748190bbdbab401280affb |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f7886be6d8819095ec62e4f2cee858 |
completed | May 3, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69f7841440f48190b4346c08855951d2 |
completed | May 3, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69f7886b27f08190ab4580f949222c93 |
completed | May 3, 2026, 5:39 p.m. |
Created at: April 29, 2026, 5:11 p.m.