Triple
T14601188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 2 complex |
E342707
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Terminal 2E |
E68455
|
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 2E | Statement: [Terminal 2 complex, hasPart, Terminal 2E]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terminal 2E Context triple: [Terminal 2 complex, hasPart, Terminal 2E]
-
A.
Terminal 2E
chosen
Terminal 2E is a major international passenger terminal at Paris Charles de Gaulle Airport, known for handling many long-haul and Air France flights.
-
B.
Terminal 3E
Terminal 3E is the international departures and arrivals concourse of Beijing Capital International Airport’s expansive Terminal 3 complex.
-
C.
Terminal 6
Terminal 6 is one of the passenger terminals at Los Angeles International Airport, serving a mix of domestic and some international flights for several major airlines.
-
D.
Terminal 3
Terminal 3 is the main international passenger terminal at José Martí International Airport in Havana, Cuba, handling most long-haul and major airline operations.
-
E.
Terminal 3
Terminal 3 is one of the main passenger terminals at Fort Lauderdale–Hollywood International Airport, serving various domestic and international airline operations.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb438748081908020ce04b869866a |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94cc9fbc819090ae4efe9bc618aa |
completed | May 8, 2026, 7:46 a.m. |
Created at: April 10, 2026, 1:25 a.m.