Triple
T23331534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malasaña |
E591455
|
entity |
| Predicate | hasMetroStationNearby |
P726
|
FINISHED |
| Object | San Bernardo |
—
|
NE NERFINISHED |
How this triple was built (2 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: San Bernardo | Statement: [Malasaña, hasMetroStationNearby, San Bernardo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Bernardo Context triple: [Malasaña, hasMetroStationNearby, San Bernardo]
-
A.
San Bernardo
San Bernardo is a major transport hub and metro station in Seville, Spain, serving as an interchange between urban rail, tram, and bus services.
-
B.
San Bernardo
San Bernardo is a commune and city in Chile that forms part of the Greater Santiago urban area and serves as an important residential and industrial hub.
-
C.
San Bernardo
chosen
San Bernardo is a Madrid Metro station serving the central Chamberí district and providing access to nearby residential and commercial areas.
-
D.
Saint Bernard
Saint Bernard is a coastal municipality in the province of Southern Leyte in the Philippines, known for its fishing communities and vulnerability to landslides and typhoons.
-
E.
Saint Bernard
The Saint Bernard is a large, gentle Swiss working dog breed historically famed for rescuing travelers in the Alps.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f197edfbbc81908cb56507cd280737 |
completed | April 29, 2026, 5:32 a.m. |
Created at: April 17, 2026, 5:15 p.m.