Triple
T38188775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London–Manchester |
E1005393
|
entity |
| Predicate | hasAirportInLondon |
P133335
|
FINISHED |
| Object | London Heathrow Airport |
—
|
NE NERFINISHED |
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: London Heathrow Airport | Statement: [London–Manchester, hasAirportInLondon, London Heathrow Airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAirportInLondon Context triple: [London–Manchester, hasAirportInLondon, London Heathrow Airport]
-
A.
majorAirportInLondon
chosen
Indicates that an airport is a primary or significant airport located within London.
-
B.
hasAirportInVicinity
Indicates that an entity is located near or served by an airport in its surrounding area.
-
C.
hasAirportAccessTo
Indicates that one location or entity has direct access to another via an airport connection or service.
-
D.
hasEndpointAirport
Indicates that something, such as a route or flight, has a specific airport as one of its terminal endpoints.
-
E.
basedInAirport
Indicates that an entity has its primary location, operations, or headquarters situated at a specific airport.
- F. None of above.
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_69f76dbc22c481908139b694ffde7a0c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fcc42cbac48190b8d3e4c9ce140838 |
completed | May 7, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69fcb0fc69c88190800453eb57a7e62c |
completed | May 7, 2026, 3:34 p.m. |
Created at: May 3, 2026, 4:29 p.m.