Triple
T7075995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madrid Metro Line 1 |
E164819
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Valdeacederas
Valdeacederas is a Madrid Metro station serving the Tetuán district in the north of Spain’s capital.
|
E641504
|
NE FINISHED |
How this triple was built (4 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: Valdeacederas | Statement: [Madrid Metro Line 1, hasStation, Valdeacederas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valdeacederas Context triple: [Madrid Metro Line 1, hasStation, Valdeacederas]
-
A.
Vallecillo
Vallecillo is a small municipality located in the Francisco Morazán Department of central Honduras.
-
B.
Valderrobres
Valderrobres is a historic town in eastern Spain known for its well-preserved medieval architecture, including a hilltop castle and Gothic bridge over the Matarraña River.
-
C.
Soto de Viñuelas
Soto de Viñuelas is a protected natural area in the Madrid region of Spain, known for its Mediterranean woodlands, wildlife, and role as a peri-urban green space near the Jarama River.
-
D.
Valle de Tena
Valle de Tena is a scenic Pyrenean valley in northeastern Spain known for its mountain landscapes, ski resorts, and outdoor recreation.
-
E.
Caleruega
Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Valdeacederas Triple: [Madrid Metro Line 1, hasStation, Valdeacederas]
Generated description
Valdeacederas is a Madrid Metro station serving the Tetuán district in the north of Spain’s capital.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Valdeacederas Target entity description: Valdeacederas is a Madrid Metro station serving the Tetuán district in the north of Spain’s capital.
-
A.
Vallecillo
Vallecillo is a small municipality located in the Francisco Morazán Department of central Honduras.
-
B.
Valderrobres
Valderrobres is a historic town in eastern Spain known for its well-preserved medieval architecture, including a hilltop castle and Gothic bridge over the Matarraña River.
-
C.
Soto de Viñuelas
Soto de Viñuelas is a protected natural area in the Madrid region of Spain, known for its Mediterranean woodlands, wildlife, and role as a peri-urban green space near the Jarama River.
-
D.
Valle de Tena
Valle de Tena is a scenic Pyrenean valley in northeastern Spain known for its mountain landscapes, ski resorts, and outdoor recreation.
-
E.
Caleruega
Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
- F. None of above. chosen
Provenance (5 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_69c6887cbc6c8190bdfac42d940f4d8a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4ce3d3c81908cbb912b256aadbf |
completed | March 27, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79c8ca5a48190bbfbf640fb1f778d |
completed | March 28, 2026, 9:17 a.m. |
| NEDg | Description generation | batch_69c79d31a9e8819096e6a3040b1852a9 |
completed | March 28, 2026, 9:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c79dc7d7d8819097e423ef70b03040 |
completed | March 28, 2026, 9:22 a.m. |
Created at: March 27, 2026, 2:40 p.m.