Triple
T33293371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moody's Mood |
E852374
|
entity |
| Predicate | usesHarmonyOf |
P63176
|
FINISHED |
| Object | "I'm in the Mood for Love" |
—
|
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: "I'm in the Mood for Love" | Statement: [Moody's Mood, usesHarmonyOf, "I'm in the Mood for Love"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesHarmonyOf Context triple: [Moody's Mood, usesHarmonyOf, "I'm in the Mood for Love"]
-
A.
usesHarmony
chosen
Indicates that one entity employs or incorporates harmony—such as coordinated or consonant combinations of elements—in relation to another entity or context.
-
B.
hasHarmony
Indicates that two or more entities are in a state of balance, agreement, or pleasing coordination with one another.
-
C.
usesHarmonics
Indicates that one entity employs harmonic frequencies or overtones of another entity or signal as part of its operation or behavior.
-
D.
usesHarmonyType
Indicates that one entity employs or is characterized by a particular type or system of harmony in relation to another.
-
E.
usesCaseHarmony
Indicates that one element selects or governs another element such that their grammatical cases are compatible or harmonized according to the language’s case system.
- 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_69f349660ff48190a4568803d0b89941 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f96badb08190994442c2aba840b1 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:33 a.m.