Triple
T15756124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teleférico de Madrid |
E381971
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | EMT Madrid |
E587312
|
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: EMT Madrid | Statement: [Teleférico de Madrid, operator, EMT Madrid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EMT Madrid Context triple: [Teleférico de Madrid, operator, EMT Madrid]
-
A.
EMT Madrid
chosen
EMT Madrid is the public bus and urban transport company serving the city of Madrid, Spain.
-
B.
EMT Valencia
EMT Valencia is the main public bus operator providing urban transport services throughout the city of Valencia, Spain.
-
C.
Humanes de Madrid
Humanes de Madrid is a municipality in the Community of Madrid, Spain, located in the metropolitan area southwest of the capital.
-
D.
BME Madrid
BME Madrid is Spain’s main stock exchange, operated by Bolsas y Mercados Españoles and based in Madrid.
-
E.
Valmadrid
Valmadrid is a small municipality in the province of Zaragoza, Aragon, Spain, situated within the semi-arid landscapes characteristic of the Campo de Belchite region.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e050b1ff4881909d5240d1d30f5c8b |
completed | April 16, 2026, 3 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff87714a8481909f8489c73ac89c11 |
completed | May 9, 2026, 7:13 p.m. |
Created at: April 10, 2026, 4:47 a.m.