Triple
T23242676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LEBB |
E581499
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | AENA |
—
|
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: AENA | Statement: [LEBB, operator, AENA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AENA Context triple: [LEBB, operator, AENA]
-
A.
Aena
chosen
Aena is the Spanish state-owned company that manages and operates the majority of airports in Spain and is one of the world’s largest airport operators by passenger traffic.
-
B.
Valencia Airport
Valencia Airport is an international airport serving the city of Valencia and the surrounding region on Spain’s eastern Mediterranean coast.
-
C.
Santander Airport
Santander Airport is a regional international airport serving the city of Santander and the Cantabria region in northern Spain.
-
D.
Burgos Airport
Burgos Airport is a regional public airport in Burgos, Spain, providing domestic air services and connecting the city to the national air transport network.
-
E.
Madrid–Torrejón Airport
Madrid–Torrejón Airport is a joint civil-military airfield near Madrid, Spain, primarily used for military, governmental, and executive aviation rather than regular commercial passenger flights.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2460556f88190be1744a84a84173f |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f192ef109881908c8fba7316c90910 |
completed | April 29, 2026, 5:11 a.m. |
Created at: April 17, 2026, 4:10 p.m.