Triple
T4229304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James Addison Halsted |
E94538
|
entity |
| Predicate | spouseFatherOccupation |
P2600
|
FINISHED |
| Object | 32nd President of the United States |
—
|
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: 32nd President of the United States | Statement: [James Addison Halsted, spouseFatherOccupation, 32nd President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseFatherOccupation Context triple: [James Addison Halsted, spouseFatherOccupation, 32nd President of the United States]
-
A.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
B.
spouseFather
Indicates that one entity is the father of another entity’s spouse.
-
C.
fatherOccupation
chosen
Indicates the type of job or profession held by a person's father.
-
D.
spouseFamily
Indicates a family relationship formed through marriage, such as between a person and their spouse’s relatives.
-
E.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
- 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_69b3453700a08190ae88792e3dc63207 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e61ccc081909b880baf1d6a0f24 |
completed | March 12, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69b347f3bd188190b0cd613e8a5c1683 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:05 p.m.