Triple
T6630164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tucson International Airport |
E149902
|
entity |
| Predicate | FAAcode |
P420
|
FINISHED |
| Object | TUS |
E600986
|
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: TUS | Statement: [Tucson International Airport, FAAcode, TUS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TUS Context triple: [Tucson International Airport, FAAcode, TUS]
-
A.
TUS
chosen
TUS is the three-letter IATA airport code for Tucson International Airport, the primary commercial airport serving Tucson, Arizona.
-
B.
Tus
Tus is an ancient city in northeastern Iran, renowned as a cultural and literary center and traditionally regarded as the birthplace and home of the Persian epic poet Ferdowsi.
-
C.
TUW
TUW is the commonly used abbreviation for the Vienna University of Technology, a major technical and scientific research university in Vienna, Austria.
-
D.
TUM SOT
TUM SOT is the TUM School of Social Sciences and Technology at the Technical University of Munich, focusing on the intersection of social sciences, technology, and policy.
-
E.
TUDN
TUDN is a Spanish-language sports television network and media brand focused on soccer and other sports, primarily serving audiences in the United States and Mexico.
- 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_69c687ee50048190aa151765bef16193 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6afa5c9b48190b645be96d446d0ca |
completed | March 27, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e44f5f9c819088cfb4fd87887766 |
completed | March 27, 2026, 8:10 p.m. |
Created at: March 27, 2026, 1:59 p.m.