Triple
T24202635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayley |
E600020
|
entity |
| Predicate | turnedFace |
P155166
|
FINISHED |
| Object | 2013-2014 (NXT run) |
—
|
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: 2013-2014 (NXT run) | Statement: [Bayley, turnedFace, 2013-2014 (NXT run)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: turnedFace Context triple: [Bayley, turnedFace, 2013-2014 (NXT run)]
-
A.
faceExpression
Indicates the specific facial expression an entity is displaying, capturing its visible emotional or expressive state.
-
B.
upset
Indicates that one entity causes another to feel distressed, unhappy, or emotionally disturbed.
-
C.
mayFace
Indicates that an entity is likely or permitted to encounter, experience, or be subjected to another entity or situation.
-
D.
emotionDisplayed
Indicates that an entity is outwardly expressing or showing a particular emotion.
-
E.
reaction
Indicates a chemical or physical process in which one set of substances or conditions transforms into another, often involving interaction, change, or response between entities.
- 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_69e288ceaab88190899d0acb5931591d |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27ca2d6708190ba20d00870d0af49 |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c43e55688190b55fc20274ed471c |
completed | April 29, 2026, 8:41 a.m. |
| PDg | Predicate description generation | batch_69f1c9834064819082024233d9c6f98f |
completed | April 29, 2026, 9:04 a.m. |
Created at: April 17, 2026, 11:36 p.m.