Triple
T26212945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwen B. Sancar |
E655537
|
entity |
| Predicate | marriedToNobelLaureateInChemistry |
P68945
|
FINISHED |
| Object | Aziz Sancar |
—
|
NE NERFINISHED |
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: Aziz Sancar | Statement: [Gwen B. Sancar, marriedToNobelLaureateInChemistry, Aziz Sancar]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToNobelLaureateInChemistry Context triple: [Gwen B. Sancar, marriedToNobelLaureateInChemistry, Aziz Sancar]
-
A.
spouseOfNobelLaureateIn
chosen
Indicates that one entity is the spouse of a Nobel Prize laureate associated with a specified field, category, or year.
-
B.
spouseNobelPrizeYear
Indicates the year in which the spouse of the referenced person received a Nobel Prize.
-
C.
marriedToPhysicist
Indicates that a person is married to someone whose profession is physicist.
-
D.
spouseNobelPrizeCountry
Indicates that the country referenced is associated with the Nobel Prize received by the spouse of the given person.
-
E.
NobelPrizeCoLaureate
Indicates that two or more individuals share the same Nobel Prize as co-recipients for a particular award and year.
- 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_69ee5b49adb4819086545280d4ef6337 |
completed | April 26, 2026, 6:36 p.m. |
| NER | Named-entity recognition | batch_69f657f653448190a945b4751af8507d |
completed | May 2, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69f6575ba12081909396036f78757a76 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 26, 2026, 8:53 p.m.