Triple
T99140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Shorter |
E2000
|
entity |
| Predicate | spouseOccupation |
P4765
|
FINISHED |
| Object | statesman |
—
|
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: statesman | Statement: [Catherine Shorter, spouseOccupation, statesman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOccupation Context triple: [Catherine Shorter, spouseOccupation, statesman]
-
A.
spouse
Indicates that two entities are married to each other in a legally or socially recognized partnership.
-
B.
fatherOccupation
Indicates the type of job or profession held by a person's father.
-
C.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
D.
spouseRelationshipEnd
Indicates that a marital relationship between two individuals has ended, such as through divorce, annulment, or separation.
-
E.
namesakeOccupation
Indicates that one entity’s occupation is the same as, or derived from, the occupation associated with the other entity’s namesake.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a253b95d4c81909d1f2bc37e799c44 |
completed | Feb. 28, 2026, 2:32 a.m. |
| PD | Predicate disambiguation | batch_69a24ebfc5a88190bdd1653b9fa541fe |
completed | Feb. 28, 2026, 2:11 a.m. |
| PDg | Predicate description generation | batch_69a253b869448190bd75a3542806b36c |
completed | Feb. 28, 2026, 2:32 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.