Triple
T14712428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michel-Gabriel Paccard |
E345582
|
entity |
| Predicate | notableOccupationCombination |
P98251
|
FINISHED |
| Object | physician and mountaineer |
—
|
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: physician and mountaineer | Statement: [Michel-Gabriel Paccard, notableOccupationCombination, physician and mountaineer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableOccupationCombination Context triple: [Michel-Gabriel Paccard, notableOccupationCombination, physician and mountaineer]
-
A.
notableOccupationContext
Indicates that the referenced occupation is notable or significant specifically within the given contextual framework or domain.
-
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.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
D.
hasOccupationCombination
chosen
Indicates that an entity holds multiple occupations or job roles in combination.
-
E.
hasBiographicalSubjectOccupation
Indicates that the biographical subject is or was engaged in the specified occupation or profession.
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb982bf248190881e21a8a0861a3f |
completed | April 14, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69de657c57ec8190ae0b9bb79a514566 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:28 a.m.