Triple
T10574710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 1 (Barcelona Metro) |
E249578
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Rocafort station
Rocafort station is an underground Barcelona Metro stop in the Eixample district, serving passengers on Line 1.
|
E875882
|
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: Rocafort station | Statement: [Line 1 (Barcelona Metro), hasStation, Rocafort station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rocafort station Context triple: [Line 1 (Barcelona Metro), hasStation, Rocafort station]
-
A.
José María Moreno station
José María Moreno station is a stop on Buenos Aires’ Line E subway serving the Caballito neighborhood.
-
B.
Pedrero station
Pedrero station is a Santiago Metro stop in Chile located near the Estadio Monumental, serving passengers on the city’s Line 5.
-
C.
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.
-
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.
Varela station
Varela station is a stop on Buenos Aires’ Line E subway, serving passengers in the city’s southeastern neighborhoods.
- 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: Rocafort station Triple: [Line 1 (Barcelona Metro), hasStation, Rocafort station]
Generated description
Rocafort station is an underground Barcelona Metro stop in the Eixample district, serving passengers on Line 1.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rocafort station Target entity description: Rocafort station is an underground Barcelona Metro stop in the Eixample district, serving passengers on Line 1.
-
A.
José María Moreno station
José María Moreno station is a stop on Buenos Aires’ Line E subway serving the Caballito neighborhood.
-
B.
Pedrero station
Pedrero station is a Santiago Metro stop in Chile located near the Estadio Monumental, serving passengers on the city’s Line 5.
-
C.
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.
-
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.
Varela station
Varela station is a stop on Buenos Aires’ Line E subway, serving passengers in the city’s southeastern neighborhoods.
- 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_69d96b5025b88190a078f5ad7b9cb3d5 |
completed | April 10, 2026, 9:27 p.m. |
| NEDg | Description generation | batch_69d96dee84f48190bf5b0cb1115a8bba |
completed | April 10, 2026, 9:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9708824208190acf75933962d690f |
completed | April 10, 2026, 9:50 p.m. |
Created at: April 6, 2026, 12:38 p.m.