Triple
T5892356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milhous |
E131019
|
entity |
| Predicate | hasNameBearerOccupation |
P12884
|
FINISHED |
| Object | 37th 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: 37th president of the United States | Statement: [Milhous, hasNameBearerOccupation, 37th president of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameBearerOccupation Context triple: [Milhous, hasNameBearerOccupation, 37th president of the United States]
-
A.
hasNotableBearerOccupation
Indicates that an entity is associated with a notable person who holds a specific occupation.
-
B.
namesakeOccupation
Indicates that one entity’s occupation is the same as, or derived from, the occupation associated with the other entity’s namesake.
-
C.
occupationalNameFor
Indicates that one entity is the name or label used to denote the occupation or profession of another entity.
-
D.
namedPersonOccupation
chosen
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
E.
isOccupationalSurname
Indicates that a surname originates from or is derived from a person’s occupation or trade.
- 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_69c00857439c819095950754176aa58a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0400f1af881908d376ea4793f6dea |
completed | March 22, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69c0334dc8248190b7394dcece362d52 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:58 p.m.