Triple
T36878414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Odette-Odile in Swan Lake |
E911405
|
entity |
| Predicate | requiresInterpretationOf |
P201642
|
FINISHED |
| Object | romantic love |
—
|
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: romantic love | Statement: [Odette-Odile in Swan Lake, requiresInterpretationOf, romantic love]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: requiresInterpretationOf Context triple: [Odette-Odile in Swan Lake, requiresInterpretationOf, romantic love]
-
A.
requiresInterpretationInLightOf
Indicates that something must be understood, evaluated, or applied by considering it in the context or framework provided by another thing.
-
B.
containsInterpretationOf
Indicates that one entity includes or embodies an interpretation or understanding of another entity.
-
C.
hasExplicitInterpretation
Indicates that something is associated with a clearly defined and unambiguous meaning or interpretation.
-
D.
intendedInterpretation
Indicates that one entity is meant to be understood or interpreted in a particular way, sense, or meaning relative to another.
-
E.
areInterpretedInPracticeBy
Indicates that something (such as a rule, concept, or specification) is given concrete meaning or applied in real-world situations by a particular agent or group.
- 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_69f76e82339881909607a65c0503d941 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a000fda03948190881b7275f249768f |
completed | May 10, 2026, 4:55 a.m. |
| PD | Predicate disambiguation | batch_6a000f607f1881908ee750d58da91690 |
completed | May 10, 2026, 4:53 a.m. |
| PDg | Predicate description generation | batch_6a000fd9602481909ffdc409ac9ca4bb |
completed | May 10, 2026, 4:55 a.m. |
Created at: May 3, 2026, 4:13 p.m.