Triple
T10644946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osona |
E250812
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Torelló
Torelló is a municipality in the comarca of Osona in Catalonia, Spain, known for its industrial heritage and scenic location in the Ter river valley.
|
E885156
|
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: Torelló | Statement: [Osona, contains, Torelló]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Torelló Context triple: [Osona, contains, Torelló]
-
A.
Gironella
Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
-
B.
Lospalos
Lospalos is a town in eastern East Timor that serves as an administrative and commercial center for the surrounding region.
-
C.
Illueca
Illueca is a small town in the province of Zaragoza, Aragon, Spain, known historically as the birthplace of Pope Benedict XIII (Pedro de Luna).
-
D.
Camarasa
Camarasa is a municipality in the province of Lleida, Catalonia, Spain, known for its reservoir and scenic location in the Noguera region.
-
E.
Corberó
Corberó is a Spanish surname most notably associated with actress Úrsula Corberó, known internationally for her role in the series "Money Heist" (La Casa de Papel).
- 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: Torelló Triple: [Osona, contains, Torelló]
Generated description
Torelló is a municipality in the comarca of Osona in Catalonia, Spain, known for its industrial heritage and scenic location in the Ter river valley.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Torelló Target entity description: Torelló is a municipality in the comarca of Osona in Catalonia, Spain, known for its industrial heritage and scenic location in the Ter river valley.
-
A.
Gironella
Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
-
B.
Lospalos
Lospalos is a town in eastern East Timor that serves as an administrative and commercial center for the surrounding region.
-
C.
Illueca
Illueca is a small town in the province of Zaragoza, Aragon, Spain, known historically as the birthplace of Pope Benedict XIII (Pedro de Luna).
-
D.
Camarasa
Camarasa is a municipality in the province of Lleida, Catalonia, Spain, known for its reservoir and scenic location in the Noguera region.
-
E.
Corberó
Corberó is a Spanish surname most notably associated with actress Úrsula Corberó, known internationally for her role in the series "Money Heist" (La Casa de Papel).
- 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfd04ca88190ac4fffd13c1f33a8 |
completed | April 8, 2026, 11:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de552d2d548190b6ade494ef2cbe7e |
completed | April 14, 2026, 2:54 p.m. |
| NEDg | Description generation | batch_69de5952f6c48190abd3b87372d54f58 |
completed | April 14, 2026, 3:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69de5ed49c9c8190a4085407f88d7a05 |
completed | April 14, 2026, 3:35 p.m. |
Created at: April 8, 2026, 9:05 p.m.