Triple
T13345448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Segrià |
E317936
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Torrefarrera
Torrefarrera is a municipality in the comarca of Segrià in Catalonia, northeastern Spain.
|
E1035323
|
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: Torrefarrera | Statement: [Segrià, hasMunicipality, Torrefarrera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Torrefarrera Context triple: [Segrià, hasMunicipality, Torrefarrera]
-
A.
Turrubares
Turrubares is a rural canton in Costa Rica known for its mountainous landscapes, agricultural activities, and low population density.
-
B.
Sierroz
Sierroz is a river in eastern France that serves as one of the tributaries feeding into Lac du Bourget.
-
C.
Calvero
Calvero is the aging, once-famous clown portrayed by Charlie Chaplin in the 1952 film "Limelight," struggling with obscurity and seeking redemption through helping a young dancer.
-
D.
Frasqueira
Frasqueira is a premium category of Madeira wine denoting long-aged, high-quality vintage bottlings.
-
E.
Abrera
Abrera is a municipality in the Baix Llobregat comarca of Catalonia, Spain, located near Barcelona and known for its industrial activity and residential areas.
- 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: Torrefarrera Triple: [Segrià, hasMunicipality, Torrefarrera]
Generated description
Torrefarrera is a municipality in the comarca of Segrià in Catalonia, northeastern Spain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Torrefarrera Target entity description: Torrefarrera is a municipality in the comarca of Segrià in Catalonia, northeastern Spain.
-
A.
Turrubares
Turrubares is a rural canton in Costa Rica known for its mountainous landscapes, agricultural activities, and low population density.
-
B.
Sierroz
Sierroz is a river in eastern France that serves as one of the tributaries feeding into Lac du Bourget.
-
C.
Calvero
Calvero is the aging, once-famous clown portrayed by Charlie Chaplin in the 1952 film "Limelight," struggling with obscurity and seeking redemption through helping a young dancer.
-
D.
Frasqueira
Frasqueira is a premium category of Madeira wine denoting long-aged, high-quality vintage bottlings.
-
E.
Abrera
Abrera is a municipality in the Baix Llobregat comarca of Catalonia, Spain, located near Barcelona and known for its industrial activity and residential areas.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e89c65c819093f3bea11d6073c5 |
completed | April 11, 2026, 1:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f439b3c8190b35fd4d097d65068 |
completed | May 3, 2026, 10:11 a.m. |
| NEDg | Description generation | batch_69f7204ac36c8190a04e921442489e9c |
completed | May 3, 2026, 10:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7221af9e881908b65ab2e7aec0c78 |
completed | May 3, 2026, 10:23 a.m. |
Created at: April 9, 2026, 9:31 p.m.