Triple
T4016163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Glenn Columbus International Airport |
E90764
|
entity |
| Predicate | isNamedForOccupationOfEponym |
P41835
|
FINISHED |
| Object | astronaut |
—
|
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: astronaut | Statement: [John Glenn Columbus International Airport, isNamedForOccupationOfEponym, astronaut]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isNamedForOccupationOfEponym Context triple: [John Glenn Columbus International Airport, isNamedForOccupationOfEponym, astronaut]
-
A.
isNamedAfterOccupation
chosen
Indicates that an entity’s name is derived from or based on a particular occupation or profession.
-
B.
isNamedFor
Indicates that one entity bears its name in honor of, or derived from, another entity.
-
C.
eponymFor
Indicates that one entity gives its name to another entity, which is then named after it.
-
D.
honorificEponym
Indicates that one entity serves as an honorific namesake for another, typically recognizing or commemorating the person or entity in whose honor something is named.
-
E.
hasAwardNamedAfter
Indicates that an entity has an award that is named in honor 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_69aed95e44088190aff7d90a151b1b20 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaec08dc8190a341809059554f84 |
completed | March 9, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69aef8fa6fec81909b1190ecbba61410 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:35 p.m.