Triple
T11071686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MNL |
E261759
|
entity |
| Predicate | terminal |
P11513
|
FINISHED |
| Object | Terminal 2 |
E261234
|
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 2 | Statement: [MNL, terminal, Terminal 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terminal 2 Context triple: [MNL, terminal, Terminal 2]
-
A.
Terminal 2
Terminal 2 is a secondary passenger terminal at Lisbon’s Humberto Delgado Airport, mainly serving low-cost and regional airlines.
-
B.
Terminal 2
Terminal 2 is a passenger terminal at Lanzarote Airport in the Canary Islands, primarily serving regional and inter-island flights.
-
C.
Terminal 2
Terminal 2 is one of the main passenger terminals at Mehrabad International Airport in Tehran, handling a significant share of the airport’s domestic flight operations.
-
D.
Terminal 2
Terminal 2 is one of the main passenger terminals at Rio de Janeiro–Galeão International Airport, handling a large share of the airport’s domestic and international flights.
-
E.
Terminal 2
chosen
Terminal 2 is a major passenger terminal at Ninoy Aquino International Airport in Manila, primarily serving as a hub for Philippine Airlines’ domestic and international 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7994bbb30819090410bd3d0fde33c |
completed | April 9, 2026, 12:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e76a49fc8190945f4770bf808b43 |
completed | April 18, 2026, 8:19 p.m. |
Created at: April 8, 2026, 9:26 p.m.