Triple
T8540869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NBO |
E202189
|
entity |
| Predicate | hasTerminal |
P182
|
FINISHED |
| Object | Terminal 1A |
E202192
|
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: Terminal 1A | Statement: [NBO, hasTerminal, Terminal 1A]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terminal 1A Context triple: [NBO, hasTerminal, Terminal 1A]
-
A.
Terminal 1A
chosen
Terminal 1A is a major passenger terminal at Jomo Kenyatta International Airport in Nairobi, primarily serving international flights and key airline operations.
-
B.
Terminal 1A
Terminal 1A is a passenger terminal at Kazan International Airport in Russia, serving as one of the airport’s main facilities for handling flights and travelers.
-
C.
Terminal 1A
Terminal 1A is a passenger terminal facility at Vienna International Airport primarily serving low-cost and regional airlines.
-
D.
Terminal 1B
Terminal 1B is a passenger terminal at Jomo Kenyatta International Airport in Nairobi, Kenya, serving scheduled commercial flights and related airport services.
-
E.
Terminal 2A
Terminal 2A is one of the sub-terminals within Barcelona–El Prat Airport’s Terminal 2 complex, serving as a passenger facility for selected airlines and flights.
- 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_69ca832461e88190a654c5e44e233aa8 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6e10bc081909a7210c577b807fb |
completed | March 31, 2026, 3:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6da3d65c819087ed6b46dfc35885 |
completed | April 2, 2026, 1:22 p.m. |
Created at: March 30, 2026, 6:18 p.m.