Triple
T7280194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Breton |
E163126
|
entity |
| Predicate | hasNotableOccupationAmongBearers |
P13522
|
FINISHED |
| Object | publisher |
—
|
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: publisher | Statement: [Le Breton, hasNotableOccupationAmongBearers, publisher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableOccupationAmongBearers Context triple: [Le Breton, hasNotableOccupationAmongBearers, publisher]
-
A.
hasNotableBearerOccupation
chosen
Indicates that an entity is associated with a notable person who holds a specific occupation.
-
B.
hasNotableProfessionDistributionIn
Indicates that the distribution or prevalence of notable professions associated with an entity is observed or characterized within a specified context, such as a location or group.
-
C.
endedOccupationOf
Indicates that one entity brought another entity’s occupation or control of a place or position to an end.
-
D.
hasNotableProfessionField
Indicates that an entity’s notable profession or occupation belongs to a particular professional field or domain.
-
E.
notableHolderOccupation
Indicates that a person notably associated with an entity (e.g., an award, office, or title) held a particular occupation or professional 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_69c6885c5964819085b209701769877f |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb339b1081909f648864e210f98e |
completed | March 27, 2026, 8:40 p.m. |
| PD | Predicate disambiguation | batch_69c6e76c5fbc8190b378830082f11cb0 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:59 p.m.