Triple
T180798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manchester Airport |
E3870
|
entity |
| Predicate | hasTerminal |
P182
|
FINISHED |
| Object | Terminal 2 |
E9864
|
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: [Manchester Airport, hasTerminal, Terminal 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terminal 2 Context triple: [Manchester Airport, hasTerminal, Terminal 2]
-
A.
Terminal 2
chosen
Terminal 2 is one of the main passenger terminals at Manchester Airport, handling a large share of the airport’s international and domestic flights.
-
B.
Terminal 2
Terminal 2 is a modern, sustainably designed passenger terminal at San Francisco International Airport known for its upgraded amenities, art installations, and improved traveler experience.
-
C.
Terminal 2
Terminal 2 is a major passenger terminal at Ronald Reagan Washington National Airport serving numerous domestic airline operations and traveler amenities.
-
D.
Terminal 2
Terminal 2 is one of the passenger terminals at Los Angeles International Airport, serving a mix of domestic and international flights with various airlines and amenities.
-
E.
Terminal 2
Terminal 2 is a major passenger terminal complex at Paris Charles de Gaulle Airport, serving numerous international and domestic flights across multiple interconnected sub-terminals.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25901a9188190b8f510bec8c8e7f2 |
completed | Feb. 28, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2fa7c7a288190bc547d5e7010732b |
completed | Feb. 28, 2026, 2:23 p.m. |
Created at: Feb. 28, 2026, 2:40 a.m.