Triple
T10804375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Tarragona |
E254925
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Cambrils
Cambrils is a coastal town and popular tourist destination on Spain’s Costa Daurada, known for its beaches, fishing port, and Mediterranean cuisine.
|
E1054713
|
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: Cambrils | Statement: [Province of Tarragona, contains, Cambrils]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cambrils Context triple: [Province of Tarragona, contains, Cambrils]
-
A.
Palamós
Palamós is a coastal town and popular tourist destination on Spain’s Costa Brava, known for its fishing port, beaches, and seafood cuisine.
-
B.
Benicàssim
Benicàssim is a coastal town in eastern Spain best known for its Mediterranean beaches and the annual Festival Internacional de Benicàssim (FIB) music festival.
-
C.
Calella
Calella is a coastal town and popular tourist destination on the Mediterranean in the Maresme comarca of Catalonia, Spain.
-
D.
Besalú
Besalú is a well-preserved medieval town in Catalonia, Spain, renowned for its Romanesque architecture and iconic 12th-century stone bridge.
-
E.
Tàrrega
Tàrrega is a historic town in Catalonia, Spain, known for its cultural festivals and medieval heritage.
- 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: Cambrils Triple: [Province of Tarragona, contains, Cambrils]
Generated description
Cambrils is a coastal town and popular tourist destination on Spain’s Costa Daurada, known for its beaches, fishing port, and Mediterranean cuisine.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cambrils Target entity description: Cambrils is a coastal town and popular tourist destination on Spain’s Costa Daurada, known for its beaches, fishing port, and Mediterranean cuisine.
-
A.
Palamós
Palamós is a coastal town and popular tourist destination on Spain’s Costa Brava, known for its fishing port, beaches, and seafood cuisine.
-
B.
Benicàssim
Benicàssim is a coastal town in eastern Spain best known for its Mediterranean beaches and the annual Festival Internacional de Benicàssim (FIB) music festival.
-
C.
Calella
Calella is a coastal town and popular tourist destination on the Mediterranean in the Maresme comarca of Catalonia, Spain.
-
D.
Besalú
Besalú is a well-preserved medieval town in Catalonia, Spain, renowned for its Romanesque architecture and iconic 12th-century stone bridge.
-
E.
Tàrrega
Tàrrega is a historic town in Catalonia, Spain, known for its cultural festivals and medieval heritage.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73370e7388190885b104fc883456e |
completed | April 9, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7941721d08190900ca872503055db |
completed | May 3, 2026, 6:29 p.m. |
| NEDg | Description generation | batch_69f796a4eac88190aa68765fd0e6dfe7 |
completed | May 3, 2026, 6:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7976846308190b1a5c056609fca34 |
completed | May 3, 2026, 6:43 p.m. |
Created at: April 8, 2026, 9:18 p.m.