Triple
T12696569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gianluigi |
E303348
|
entity |
| Predicate | associatedWithOccupationOfNotableBearers |
P13522
|
FINISHED |
| Object | football goalkeeper |
—
|
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: football goalkeeper | Statement: [Gianluigi, associatedWithOccupationOfNotableBearers, football goalkeeper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithOccupationOfNotableBearers Context triple: [Gianluigi, associatedWithOccupationOfNotableBearers, football goalkeeper]
-
A.
associatedWithNotableBearerNationality
Indicates that an entity is connected to the nationality of a notable bearer of a related name or title.
-
B.
notableHolderOccupation
Indicates that a person notably associated with an entity (e.g., an award, office, or title) held a particular occupation or professional role.
-
C.
notableOccupationContext
Indicates that the referenced occupation is notable or significant specifically within the given contextual framework or domain.
-
D.
hasNotableBearerOccupation
chosen
Indicates that an entity is associated with a notable person who holds a specific occupation.
-
E.
namedPersonOccupation
Indicates that a person is explicitly identified as having a particular occupation or job role.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d962a32c6481908ddaddae4ea267bf |
completed | April 10, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69d960be63f081908a5ef5ef17a311bf |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:22 p.m.