Triple
T13802464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Social Point |
E331672
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Barcelona, Spain |
E9407
|
NE FINISHED |
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: Barcelona, Spain | Statement: [Social Point, headquartersLocation, Barcelona, Spain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barcelona, Spain Context triple: [Social Point, headquartersLocation, Barcelona, Spain]
-
A.
Barcelona
chosen
Barcelona is a major Spanish Mediterranean city renowned for its distinctive Catalan culture, Gaudí architecture, and vibrant arts and nightlife scenes.
-
B.
El Masnou, Spain
El Masnou, Spain is a coastal town in the province of Barcelona, Catalonia, known for its Mediterranean beaches and marina.
-
C.
Martorell, Spain
Martorell, Spain is a town in Catalonia best known as a major automotive manufacturing hub and home to SEAT’s main production plant.
-
D.
Barcelonès
Barcelonès is a highly urbanized comarca in Catalonia that includes the city of Barcelona and serves as one of the most densely populated areas in Spain.
-
E.
42 Barcelona
42 Barcelona is a tuition-free, peer-to-peer programming school in Barcelona that follows the innovative, project-based learning model of the international 42 network.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d81c59f8808190a851bc56afdc55e9 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de025ded6c8190851c108ae559a355 |
completed | April 14, 2026, 9:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c0e5251c81909f3f40dcdea1772f |
completed | May 3, 2026, 9:40 p.m. |
Created at: April 9, 2026, 10:12 p.m.