Triple
T10644773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maresme |
E250808
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Tordera
Tordera is a municipality in the Maresme comarca of Catalonia, Spain, known for its rural landscapes and proximity to the Costa Brava.
|
E878556
|
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: Tordera | Statement: [Maresme, hasMunicipality, Tordera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tordera Context triple: [Maresme, hasMunicipality, Tordera]
-
A.
Noguera Pallaresa
Noguera Pallaresa is a river in the Catalan Pyrenees of northeastern Spain, renowned for its whitewater rafting and kayaking.
-
B.
Santpedor
Santpedor is a small town in Catalonia, Spain, best known internationally as the birthplace of football manager Pep Guardiola.
-
C.
Gironella
Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
-
D.
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).
-
E.
Capalonga
Capalonga is a coastal municipality in the Philippine province of Camarines Norte known for its fishing communities, natural attractions, and religious pilgrimage sites.
- 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: Tordera Triple: [Maresme, hasMunicipality, Tordera]
Generated description
Tordera is a municipality in the Maresme comarca of Catalonia, Spain, known for its rural landscapes and proximity to the Costa Brava.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tordera Target entity description: Tordera is a municipality in the Maresme comarca of Catalonia, Spain, known for its rural landscapes and proximity to the Costa Brava.
-
A.
Noguera Pallaresa
Noguera Pallaresa is a river in the Catalan Pyrenees of northeastern Spain, renowned for its whitewater rafting and kayaking.
-
B.
Santpedor
Santpedor is a small town in Catalonia, Spain, best known internationally as the birthplace of football manager Pep Guardiola.
-
C.
Gironella
Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
-
D.
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).
-
E.
Capalonga
Capalonga is a coastal municipality in the Philippine province of Camarines Norte known for its fishing communities, natural attractions, and religious pilgrimage sites.
- 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_69d988530f288190b8150d159f723a74 |
completed | April 10, 2026, 11:31 p.m. |
| NEDg | Description generation | batch_69d98afa316c8190b9645401d8e43f59 |
completed | April 10, 2026, 11:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d98c013348819094bde38a057257b4 |
completed | April 10, 2026, 11:47 p.m. |
Created at: April 8, 2026, 9:05 p.m.