Triple
T16728061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marrakesh Menara Airport |
E406513
|
entity |
| Predicate | servesAsFocusCityFor |
P1655
|
FINISHED |
| Object | easyJet |
E6907
|
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: easyJet | Statement: [Marrakesh Menara Airport, servesAsFocusCityFor, easyJet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: easyJet Context triple: [Marrakesh Menara Airport, servesAsFocusCityFor, easyJet]
-
A.
easyJet
chosen
easyJet is a major British low-cost airline operating extensive domestic and European routes.
-
B.
Ryanair
Ryanair is a major Irish low-cost airline known for its extensive network of short-haul flights across Europe.
-
C.
Flybe
Flybe was a British regional airline that operated short-haul flights across the UK and Europe before ceasing operations.
-
D.
Wizz Air
Wizz Air is a Hungarian ultra-low-cost airline known for operating an extensive network of budget flights across Europe and surrounding regions.
-
E.
EasyFly
EasyFly is a Colombian regional airline that operates domestic flights connecting smaller cities and regional hubs across the country.
- 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_69d8838f242881908abd8bc138795886 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e38749baa48190892b2e2b978f6eb6 |
completed | April 18, 2026, 1:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a519a89081909456bb6fc25d9234 |
completed | May 10, 2026, 3:32 p.m. |
Created at: April 10, 2026, 5:20 a.m.