Triple
T12099147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dana Air |
E288145
|
entity |
| Predicate | callsign |
P1565
|
FINISHED |
| Object |
DANACO
DANACO is the radio callsign used by Dana Air, a Nigerian domestic airline.
|
E963350
|
NE FINISHED |
How this triple was built (4 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: DANACO | Statement: [Dana Air, callsign, DANACO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DANACO Context triple: [Dana Air, callsign, DANACO]
-
A.
DANA
DANA is a popular Indonesian digital wallet and mobile payment platform used for cashless transactions, bill payments, and online purchases.
-
B.
Daksum
Daksum is a scenic hill station and forested valley in Jammu and Kashmir, India, known for its lush landscapes, trout-filled streams, and trekking routes in the Anantnag region.
-
C.
Dan Dan
Dan Dan is the central protagonist of the film "Coming Home," whose personal journey and experiences drive the emotional core of the story.
-
D.
Dasani
Dasani is a bottled water brand owned by The Coca-Cola Company, known for its purified water with added minerals for taste and wide distribution in retail markets.
-
E.
Concanen
Concanen is a surname most notably associated with R. Luke Concanen, an Irish-born Roman Catholic bishop and the first Bishop of New York in the early 19th century.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: DANACO Triple: [Dana Air, callsign, DANACO]
Generated description
DANACO is the radio callsign used by Dana Air, a Nigerian domestic airline.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DANACO Target entity description: DANACO is the radio callsign used by Dana Air, a Nigerian domestic airline.
-
A.
DANA
DANA is a popular Indonesian digital wallet and mobile payment platform used for cashless transactions, bill payments, and online purchases.
-
B.
Daksum
Daksum is a scenic hill station and forested valley in Jammu and Kashmir, India, known for its lush landscapes, trout-filled streams, and trekking routes in the Anantnag region.
-
C.
Dan Dan
Dan Dan is the central protagonist of the film "Coming Home," whose personal journey and experiences drive the emotional core of the story.
-
D.
Dasani
Dasani is a bottled water brand owned by The Coca-Cola Company, known for its purified water with added minerals for taste and wide distribution in retail markets.
-
E.
Concanen
Concanen is a surname most notably associated with R. Luke Concanen, an Irish-born Roman Catholic bishop and the first Bishop of New York in the early 19th century.
- F. None of above. chosen
Provenance (5 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9155465388190bbe52453c9b11912 |
completed | April 10, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f6724de481909fe29e3278136ea2 |
completed | May 2, 2026, 1:04 p.m. |
| NEDg | Description generation | batch_69f5fe53d47c8190896a9abf8cc4bc31 |
completed | May 2, 2026, 1:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f5ffc2cfd08190b87eccd3a73afc77 |
completed | May 2, 2026, 1:44 p.m. |
Created at: April 8, 2026, 9:48 p.m.