Triple
T31534318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Keith Broke His Leg |
E804562
|
entity |
| Predicate | metaAspect |
P128794
|
FINISHED |
| Object | blurs line between actor and character |
—
|
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: blurs line between actor and character | Statement: [Keith Broke His Leg, metaAspect, blurs line between actor and character]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: metaAspect Context triple: [Keith Broke His Leg, metaAspect, blurs line between actor and character]
-
A.
symbolicAspect
Indicates that one entity functions as a symbol or emblem that represents, expresses, or conveys a particular meaning, quality, or concept of another entity.
-
B.
coversAspect
Indicates that one entity addresses, includes, or deals with a particular aspect or facet of another entity or topic.
-
C.
uniqueAspect
Indicates that the subject possesses a distinctive feature or characteristic that sets it apart from others.
-
D.
conceptualAspect
chosen
Indicates that one entity represents an abstract, conceptual, or non-physical aspect, feature, or dimension of another entity.
-
E.
onAspect
Indicates that one entity is positioned on a particular side, surface, or facet 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_69f348d03ef88190a2b73d7b94b9e02d |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a8055f8081908f635fe04654b5fe |
completed | May 3, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69f6a75656e081908739ed9e2f600e42 |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 30, 2026, 10:02 p.m.