Triple
T10574725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 1 (Barcelona Metro) |
E249578
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Baró de Viver station
Baró de Viver station is a Barcelona Metro stop serving the Baró de Viver neighborhood in the Sant Andreu district of Barcelona, Spain.
|
E879872
|
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: Baró de Viver station | Statement: [Line 1 (Barcelona Metro), hasStation, Baró de Viver station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baró de Viver station Context triple: [Line 1 (Barcelona Metro), hasStation, Baró de Viver station]
-
A.
Rocafort station
Rocafort station is an underground Barcelona Metro stop in the Eixample district, serving passengers on Line 1.
-
B.
Francisco Goitia station
Francisco Goitia station is a stop on the Xochimilco Light Rail system in Mexico City, serving local commuters in the southern part of the city.
-
C.
Varela station
Varela station is a stop on Buenos Aires’ Line E subway, serving passengers in the city’s southeastern neighborhoods.
-
D.
Martínez Nadal station
Martínez Nadal station is a rapid transit stop on the Tren Urbano system serving the San Juan metropolitan area in Puerto Rico.
-
E.
Olleros station
Olleros station is a stop on Buenos Aires’ Line D subway, serving the Palermo and Colegiales neighborhoods in Argentina’s capital.
- 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: Baró de Viver station Triple: [Line 1 (Barcelona Metro), hasStation, Baró de Viver station]
Generated description
Baró de Viver station is a Barcelona Metro stop serving the Baró de Viver neighborhood in the Sant Andreu district of Barcelona, Spain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Baró de Viver station Target entity description: Baró de Viver station is a Barcelona Metro stop serving the Baró de Viver neighborhood in the Sant Andreu district of Barcelona, Spain.
-
A.
Rocafort station
Rocafort station is an underground Barcelona Metro stop in the Eixample district, serving passengers on Line 1.
-
B.
Francisco Goitia station
Francisco Goitia station is a stop on the Xochimilco Light Rail system in Mexico City, serving local commuters in the southern part of the city.
-
C.
Varela station
Varela station is a stop on Buenos Aires’ Line E subway, serving passengers in the city’s southeastern neighborhoods.
-
D.
Martínez Nadal station
Martínez Nadal station is a rapid transit stop on the Tren Urbano system serving the San Juan metropolitan area in Puerto Rico.
-
E.
Olleros station
Olleros station is a stop on Buenos Aires’ Line D subway, serving the Palermo and Colegiales neighborhoods in Argentina’s capital.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d52749dda08190b0c9627a931c5848 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9988fca088190b13b651985677a6a |
completed | April 11, 2026, 12:40 a.m. |
| NEDg | Description generation | batch_69d99e8312188190bec3090f34a7b9b9 |
completed | April 11, 2026, 1:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d99f50e0888190b8e7b2547e1526af |
completed | April 11, 2026, 1:09 a.m. |
Created at: April 6, 2026, 12:38 p.m.