Triple
T28095661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Igor I. Sikorsky Memorial Airport |
E710081
|
entity |
| Predicate | isNamedAfterFull |
P107513
|
FINISHED |
| Object | Igor Ivanovich Sikorsky |
—
|
NE NERFINISHED |
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: Igor Ivanovich Sikorsky | Statement: [Igor I. Sikorsky Memorial Airport, isNamedAfterFull, Igor Ivanovich Sikorsky]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isNamedAfterFull Context triple: [Igor I. Sikorsky Memorial Airport, isNamedAfterFull, Igor Ivanovich Sikorsky]
-
A.
isNamedFor
Indicates that one entity bears its name in honor of, or derived from, another entity.
-
B.
hasNamedAfterPerson
chosen
Indicates that one entity is named in honor of, or derived from the name of, a specific person.
-
C.
hasPartiallyNamedAfter
Indicates that one entity is partially named in reference to another entity, such that only a portion of its name derives from or honors the other.
-
D.
isNamedForPersonFrom
Indicates that an entity is named after a person who originates from a specified place or region.
-
E.
fieldOfNamedAfter
Indicates that a field of study or professional discipline is named in honor of a particular person or 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_69ef9b70fd108190a875953b2e50ca91 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f65876c52c8190bc889c7a67bd07f3 |
completed | May 2, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69f6575d89788190aca478e4aea05a65 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 27, 2026, 9:01 p.m.