Triple
T12407122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant Governor of Iowa |
E296416
|
entity |
| Predicate | firstFemaleOfficeHolderStartTime |
P48982
|
FINISHED |
| Object | 1987 |
—
|
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: 1987 | Statement: [Lieutenant Governor of Iowa, firstFemaleOfficeHolderStartTime, 1987]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstFemaleOfficeHolderStartTime Context triple: [Lieutenant Governor of Iowa, firstFemaleOfficeHolderStartTime, 1987]
-
A.
firstFemaleHolderDate
chosen
Indicates the date on which the first female individual came to hold a given position, title, or role.
-
B.
isFirstFemaleHolderOfOffice
Indicates that a person is the first woman ever to hold a particular office or position.
-
C.
ageAtStartOfFirstLadyRole
Indicates the age a person was when they first assumed the role of First Lady.
-
D.
firstLadyTermApproximateStart
Indicates the approximate date when a person’s tenure as First Lady began.
-
E.
startTimeAsFirstLadyOfCalifornia
Indicates the date and time when an individual first assumed the role of First Lady of California.
- 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e1888b48190bd750f839a26e99e |
completed | April 10, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69d94d354b488190adc83fb4f2770dd5 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:55 p.m.