Triple
T35806636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chokeslam |
E1035117
|
entity |
| Predicate | typicalImpactArea |
P1586
|
FINISHED |
| Object | wrestling ring mat |
—
|
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: wrestling ring mat | Statement: [Chokeslam, typicalImpactArea, wrestling ring mat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalImpactArea Context triple: [Chokeslam, typicalImpactArea, wrestling ring mat]
-
A.
hasImpactArea
Indicates that an entity affects, influences, or has consequences within a specific area, domain, or scope.
-
B.
affectedArea
chosen
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
C.
impactRegion
Indicates the geographic or spatial area that is affected or influenced by a particular event, action, or phenomenon.
-
D.
sectorMostAffected
Indicates that a particular sector is the one experiencing the greatest impact or disruption relative to others in a given context.
-
E.
typicalLevelOfInfluence
Indicates the usual degree or strength of influence one entity exerts over another or within a given context.
- 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_69f76e1762408190b885a8456862e372 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:06 p.m.