Triple
T25573556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kazi Nazrul Islam Airport |
E641041
|
entity |
| Predicate | namedForEponymFullName |
P47121
|
FINISHED |
| Object | Kazi Nazrul Islam |
—
|
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: Kazi Nazrul Islam | Statement: [Kazi Nazrul Islam Airport, namedForEponymFullName, Kazi Nazrul Islam]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: namedForEponymFullName Context triple: [Kazi Nazrul Islam Airport, namedForEponymFullName, Kazi Nazrul Islam]
-
A.
isNamedForEponymRole
Indicates that one entity bears a name derived from another entity that serves as its eponym or namesake.
-
B.
eponymKnownFor
Indicates that a person or entity is widely recognized or named as the source or inspiration for something else (such as a concept, place, or object).
-
C.
eponymProfession
Indicates that a person’s profession is the source of an eponym, i.e., a word or name derived from that professional role.
-
D.
speciesEponym
Indicates that a species is named in honor of a particular person or entity.
-
E.
eponymFor
chosen
Indicates that one entity gives its name to another entity, which is then named after it.
- 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_69e75dc281bc819095ec04dc0c3a94d0 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f92ee22c81909f1de62c9023af1b |
completed | May 2, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69f5afec3e94819080d9ba86cf8c866e |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 21, 2026, 3:59 p.m.