Triple
T15499709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danville Regional Airport |
E378916
|
entity |
| Predicate | pushpinLabel |
P9248
|
FINISHED |
| Object | DAN |
E1160118
|
NE 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: DAN | Statement: [Danville Regional Airport, pushpinLabel, DAN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DAN Context triple: [Danville Regional Airport, pushpinLabel, DAN]
-
A.
DAN
DAN is a neutron-detecting scientific instrument on NASA's Curiosity rover used to measure subsurface hydrogen and infer the presence of water on Mars.
-
B.
DAN
DAN is the station code for Dane Road tram stop on Greater Manchester's Metrolink light rail network in England.
-
C.
DAN
chosen
DAN is the IATA airport code for Danville Regional Airport, a public airport serving Danville, Virginia, in the United States.
-
D.
Dan
Dan is the protagonist of Cory Doctorow's science fiction novel "Down and Out in the Magic Kingdom," a post-scarcity future resident of a reputation-based society centered around a Disney theme park.
-
E.
Dan
Dan is a male given name commonly used in English-speaking countries, often as a short form of Daniel.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcb4e8c81908e4ab463e3ae252b |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff454ca0f0819088ba846a448dda2e |
completed | May 9, 2026, 2:31 p.m. |
Created at: April 10, 2026, 3:54 a.m.