Triple
T3053586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eric Cantona |
E60426
|
entity |
| Predicate | disciplinaryAction |
P13534
|
FINISHED |
| Object | banned from football for several months in 1995 |
—
|
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: banned from football for several months in 1995 | Statement: [Eric Cantona, disciplinaryAction, banned from football for several months in 1995]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disciplinaryAction Context triple: [Eric Cantona, disciplinaryAction, banned from football for several months in 1995]
-
A.
disciplinaryMethod
Indicates a method or approach used to discipline, correct, or control another party’s behavior.
-
B.
disciplinaryRecord
chosen
Indicates that there exists a documented history of disciplinary actions or sanctions associated with an entity.
-
C.
isMultidisciplinary
Indicates that something involves or integrates multiple distinct academic or professional disciplines in its approach or composition.
-
D.
subDisciplineOf
Indicates that one discipline is a more specialized or narrower field within another, broader discipline.
-
E.
academicFocus
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
- 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_69ad8578137c81908259dcb27c7d6d7c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ad9bf51b5081908ce355a76cfa9e3c |
completed | March 8, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69ad962195388190856013a2519c2b0f |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:01 p.m.