Triple
T38059331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Newcastle Airport Metro station |
E950296
|
entity |
| Predicate | hasCoveredWalkwayToAirport |
P57943
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [Newcastle Airport Metro station, hasCoveredWalkwayToAirport, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCoveredWalkwayToAirport Context triple: [Newcastle Airport Metro station, hasCoveredWalkwayToAirport, yes]
-
A.
hasCoveredWalkway
Indicates that one place or structure is connected to another by a walkway that is sheltered or roofed.
-
B.
hasAirportWalkway
chosen
Indicates that there is a pedestrian walkway connection associated with or leading to an airport.
-
C.
hasRunwayAccessVia
Indicates that an entity has access to a runway by means of a specified connecting route, facility, or intermediary.
-
D.
hasWalkedRunwayFor
Indicates that one entity has modeled or appeared on the fashion runway in a show organized by another entity.
-
E.
hasRunwayAccessTo
Indicates that one location or facility is directly connected to another via a usable runway, allowing aircraft to move between them without leaving runway infrastructure.
- 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_69f76f01e63c819093b6012fc974f35a |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:21 p.m.