Triple
T26346792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ECW One Night Stand 2006 main event |
E662796
|
entity |
| Predicate | interferenceType |
P41951
|
FINISHED |
| Object | spear on John Cena |
—
|
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: spear on John Cena | Statement: [ECW One Night Stand 2006 main event, interferenceType, spear on John Cena]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: interferenceType Context triple: [ECW One Night Stand 2006 main event, interferenceType, spear on John Cena]
-
A.
hasInterference
Indicates that one entity negatively affects or obstructs the normal function, performance, or occurrence of another entity or process.
-
B.
interventionType
chosen
Indicates the specific kind or category of action, treatment, or measure applied in an intervention.
-
C.
يتداخل
Indicates that one entity overlaps or intersects spatially, temporally, or conceptually with another entity.
-
D.
interactionBetween
Indicates a reciprocal or mutual action, influence, or communication occurring between two or more entities.
-
E.
interferenceManagement
Indicates the management or mitigation of disruptive effects one entity’s signals or actions have on another’s performance or operation.
- 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_69ee81304194819092e20e0fae3aee07 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f69383222c81909d8baa04129d5c81 |
completed | May 3, 2026, 12:14 a.m. |
| PD | Predicate disambiguation | batch_69f690eb1e948190aab41a89969519a5 |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 26, 2026, 10:42 p.m.