Triple
T18338161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zoé Laurier |
E439327
|
entity |
| Predicate | spouseOfOfficeholderNumber |
P131425
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Zoé Laurier, spouseOfOfficeholderNumber, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOfOfficeholderNumber Context triple: [Zoé Laurier, spouseOfOfficeholderNumber, 7]
-
A.
spouseNumberOfTermsInOffice
Indicates the number of distinct terms in office that the spouse of the referenced entity has served.
-
B.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
-
C.
spouseOfOfficeHolderJurisdiction
Indicates that one person is the spouse of a public office holder, with the relationship specifically tied to the jurisdiction in which that office is held.
-
D.
spouseLaterOffice
Indicates that one person’s spouse held a particular office or position at a later time than the person in question.
-
E.
spouseCount
Indicates the number of spouses an entity has.
- F. None of above. chosen
Provenance (4 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_69d8b9175fec8190af865699b4e64d8c |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50ecf3b4c81909372630ab634dd43 |
completed | April 19, 2026, 5:20 p.m. |
| PD | Predicate disambiguation | batch_69e44fe91bc08190906518e1b120fcf0 |
completed | April 19, 2026, 3:45 a.m. |
| PDg | Predicate description generation | batch_69e4561d665081908ec555344e76d82b |
completed | April 19, 2026, 4:12 a.m. |
Created at: April 10, 2026, 10:37 a.m.