Triple
T11263963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stop Flirting |
E266633
|
entity |
| Predicate | targetMood |
P63081
|
FINISHED |
| Object | entertainment |
—
|
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: entertainment | Statement: [Stop Flirting, targetMood, entertainment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetMood Context triple: [Stop Flirting, targetMood, entertainment]
-
A.
hasMood
Indicates that an entity is experiencing or characterized by a particular emotional or affective state.
-
B.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
-
C.
intendedEmotion
chosen
Indicates the emotion that an action, expression, or communication is meant to evoke in its target, regardless of the actual emotion experienced.
-
D.
featuresMood
Indicates that something includes, presents, or conveys a particular mood or emotional atmosphere.
-
E.
supportsMood
Indicates that one entity helps maintain, enhance, or positively influence the emotional state or mood 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_69d6aac7953c8190b82caf9d7640fdf9 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e94d56048190bf808e1bc2188714 |
completed | April 9, 2026, 6 p.m. |
| PD | Predicate disambiguation | batch_69d7879bc56c8190b2e8d2193f29de05 |
completed | April 9, 2026, 11:03 a.m. |
Created at: April 8, 2026, 9:31 p.m.