Triple
T7587931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hot. Cool. Yours. |
E179662
|
entity |
| Predicate | intendedToExpress |
P4750
|
FINISHED |
| Object | passion of the Olympic Games |
—
|
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: passion of the Olympic Games | Statement: [Hot. Cool. Yours., intendedToExpress, passion of the Olympic Games]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intendedToExpress Context triple: [Hot. Cool. Yours., intendedToExpress, passion of the Olympic Games]
-
A.
intendedInterpretation
Indicates that one entity is meant to be understood or interpreted in a particular way, sense, or meaning relative to another.
-
B.
valueExpressed
Indicates that a particular value, opinion, or quantitative measure is articulated, represented, or made explicit by an entity or expression.
-
C.
expresses
chosen
Indicates that one entity conveys, communicates, or articulates a thought, feeling, or idea through another medium or form.
-
D.
refersSpecificallyTo
Indicates that one entity makes an explicit, precise reference to another particular entity, distinguishing it from more general or ambiguous references.
-
E.
containsInterpretationOf
Indicates that one entity includes or embodies an interpretation or understanding of another entity.
- 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_69c69f335248819093c1006f30513708 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f99875908190b09584cf13ea1e08 |
completed | March 27, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e04c2c8190a889d928515d9b8e |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:52 p.m.