Triple
T6624156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Foraker |
E149752
|
entity |
| Predicate | personNamedAfterOccupation |
P68977
|
FINISHED |
| Object | U.S. senator from Ohio |
—
|
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: U.S. senator from Ohio | Statement: [Mount Foraker, personNamedAfterOccupation, U.S. senator from Ohio]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: personNamedAfterOccupation Context triple: [Mount Foraker, personNamedAfterOccupation, U.S. senator from Ohio]
-
A.
isNamedAfterOccupation
Indicates that an entity’s name is derived from or based on a particular occupation or profession.
-
B.
hasAwardNamedAfter
Indicates that an entity has an award that is named in honor of another entity.
-
C.
namedPersonOccupation
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
D.
notablePlaceNamedAfter
chosen
Indicates that a notable place (such as a city, building, or landmark) is named in honor of or derived from the name of a particular entity.
-
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.
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_69c687ed8a9c81908bb671717cb192ef |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6bdb88cc881908f35648c15a7dc85 |
completed | March 27, 2026, 5:26 p.m. |
| PD | Predicate disambiguation | batch_69c6ad007c1c8190af425f51011c7ad1 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:58 p.m.