Triple
T6395628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | C-5 |
E143932
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
La Serna
La Serna is a station on Madrid Metro’s Line C-5 commuter rail corridor serving the Fuenlabrada area in the Community of Madrid, Spain.
|
E598195
|
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: La Serna | Statement: [C-5, hasStation, La Serna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: La Serna Context triple: [C-5, hasStation, La Serna]
-
A.
Hinojosa
Hinojosa is a Spanish surname historically associated with figures such as José de la Serna e Hinojosa, the last viceroy of Peru.
-
B.
Molinero
Molinero is a Spanish surname that corresponds to the German surname Müller, both historically referring to the occupation of a miller.
-
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.
Rivas
Rivas is a city in southwestern Nicaragua known as a regional commercial center and gateway between Lake Nicaragua and the Pacific coast.
-
E.
Oyón
Oyón is a town in Peru that serves as the administrative and commercial center of the surrounding Oyón Province in the highlands.
- 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: La Serna Triple: [C-5, hasStation, La Serna]
Generated description
La Serna is a station on Madrid Metro’s Line C-5 commuter rail corridor serving the Fuenlabrada area in the Community of Madrid, Spain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: La Serna Target entity description: La Serna is a station on Madrid Metro’s Line C-5 commuter rail corridor serving the Fuenlabrada area in the Community of Madrid, Spain.
-
A.
Hinojosa
Hinojosa is a Spanish surname historically associated with figures such as José de la Serna e Hinojosa, the last viceroy of Peru.
-
B.
Molinero
Molinero is a Spanish surname that corresponds to the German surname Müller, both historically referring to the occupation of a miller.
-
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.
Rivas
Rivas is a city in southwestern Nicaragua known as a regional commercial center and gateway between Lake Nicaragua and the Pacific coast.
-
E.
Oyón
Oyón is a town in Peru that serves as the administrative and commercial center of the surrounding Oyón Province in the highlands.
- 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_69c008db906c819096f3597d55d95432 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0688275d0819086b58123c743a6db |
completed | March 22, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c669dbb0708190b0651c524a80a251 |
completed | March 27, 2026, 11:28 a.m. |
| NEDg | Description generation | batch_69c66b56e888819086c21652ed216bf9 |
completed | March 27, 2026, 11:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c66b8314348190956604c935c648f7 |
completed | March 27, 2026, 11:35 a.m. |
Created at: March 22, 2026, 4:35 p.m.