Triple
T21985179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Germaine Corblet |
E542939
|
entity |
| Predicate | spousePositionNumber |
P131425
|
FINISHED |
| Object | 17th President of the French Republic |
—
|
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: 17th President of the French Republic | Statement: [Germaine Corblet, spousePositionNumber, 17th President of the French Republic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spousePositionNumber Context triple: [Germaine Corblet, spousePositionNumber, 17th President of the French Republic]
-
A.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
-
B.
spouseOfOfficeholderNumber
chosen
Indicates that one entity is the spouse of a specific officeholder identified by their ordinal number in holding a particular office.
-
C.
positionHeldBySpouse
Indicates that a particular position, role, or office is or was held by the spouse of a given person.
-
D.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
E.
hasSpousePositionInFamily
Indicates that a person’s spouse holds a specific role or position within the family structure.
- 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_69e0c48136b081908831fa907cc02e18 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12708cdcc81909511d9f81bd8f20e |
completed | April 28, 2026, 9:30 p.m. |
| PD | Predicate disambiguation | batch_69e6f6154e408190acc5b2c278acaff4 |
completed | April 21, 2026, 3:59 a.m. |
Created at: April 16, 2026, 8:04 p.m.