Triple
T10574718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 1 (Barcelona Metro) |
E249578
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Clot station
Clot station is a Barcelona Metro and commuter rail interchange station serving the Clot neighborhood in the Sant Martí district of Barcelona, Spain.
|
E871696
|
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: Clot station | Statement: [Line 1 (Barcelona Metro), hasStation, Clot station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clot station Context triple: [Line 1 (Barcelona Metro), hasStation, Clot station]
-
A.
Bonaventure station
Bonaventure station is a major Montreal Metro station in downtown Montreal, Quebec, serving as an important transit hub near key commercial and entertainment venues.
-
B.
Fabre station
Fabre station is a Montreal Metro station on the Blue Line serving the Rosemont–La Petite-Patrie borough.
-
C.
Chardon-Lagache station
Chardon-Lagache station is a Paris Métro station in the 16th arrondissement, serving the residential Auteuil area.
-
D.
Lison station
Lison station is a railway station in Normandy, France, serving as a regional junction on the French rail network.
-
E.
Loria station
Loria station is a stop on Buenos Aires’ Line A subway, serving passengers in the Balvanera neighborhood of the city.
- 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: Clot station Triple: [Line 1 (Barcelona Metro), hasStation, Clot station]
Generated description
Clot station is a Barcelona Metro and commuter rail interchange station serving the Clot neighborhood in the Sant Martí district of Barcelona, Spain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Clot station Target entity description: Clot station is a Barcelona Metro and commuter rail interchange station serving the Clot neighborhood in the Sant Martí district of Barcelona, Spain.
-
A.
Bonaventure station
Bonaventure station is a major Montreal Metro station in downtown Montreal, Quebec, serving as an important transit hub near key commercial and entertainment venues.
-
B.
Fabre station
Fabre station is a Montreal Metro station on the Blue Line serving the Rosemont–La Petite-Patrie borough.
-
C.
Chardon-Lagache station
Chardon-Lagache station is a Paris Métro station in the 16th arrondissement, serving the residential Auteuil area.
-
D.
Lison station
Lison station is a railway station in Normandy, France, serving as a regional junction on the French rail network.
-
E.
Loria station
Loria station is a stop on Buenos Aires’ Line A subway, serving passengers in the Balvanera neighborhood of the city.
- 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_69d94b5d89748190bb398943e4a16e9b |
completed | April 10, 2026, 7:11 p.m. |
| NEDg | Description generation | batch_69d94e1502108190a81bfa1d5a425e5a |
completed | April 10, 2026, 7:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d94f0bb6888190b4038df6dcd96d33 |
completed | April 10, 2026, 7:27 p.m. |
Created at: April 6, 2026, 12:38 p.m.