Triple
T34998782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Mayor |
E1009609
|
entity |
| Predicate | hasEmotionFace |
P23736
|
FINISHED |
| Object | happy face |
—
|
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: happy face | Statement: [The Mayor, hasEmotionFace, happy face]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmotionFace Context triple: [The Mayor, hasEmotionFace, happy face]
-
A.
hasTypeOfEmotion
Indicates that an entity experiences, expresses, or is associated with a particular kind or category of emotion.
-
B.
hasFace
Indicates that one entity possesses, displays, or is characterized by a face.
-
C.
faceExpression
chosen
Indicates the specific facial expression an entity is displaying, capturing its visible emotional or expressive state.
-
D.
requiresEmotion
Indicates that one entity’s occurrence, validity, or performance depends on the presence or experience of a particular emotion in another entity.
-
E.
hasEmotionSimulation
Indicates that an entity is capable of generating or exhibiting a simulated emotional state rather than a genuine one.
- 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_69f76dcb716881909f75e4fd60ab2284 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4:01 p.m.