Triple
T344409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | easyJet |
E6907
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Luton |
E51115
|
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: Luton | Statement: [easyJet, headquartersLocation, Luton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luton Context triple: [easyJet, headquartersLocation, Luton]
-
A.
Luton
chosen
Luton is a large town in Bedfordshire, England, known for its international airport and diverse urban population.
-
B.
Hertford
Hertford is a historic market town and the county town of Hertfordshire in southern England.
-
C.
Dartford
Dartford is a historic market and industrial town in southeast England, situated on the River Darent and serving as a key commuter hub for London.
-
D.
Wantage
Wantage is a historic market town in Oxfordshire, England, best known as the birthplace of King Alfred the Great.
-
E.
Slough
Slough is a large industrial and commercial town in southern England, known for its diverse population and proximity to London and Heathrow Airport.
- 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_69a2e7951ba08190960e90823b5078f3 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eb01261c81909280128b5ce75eff |
completed | Feb. 28, 2026, 1:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a48a72e3c48190bbdf104ff607262b |
completed | March 1, 2026, 6:50 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.