Triple
T13345464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Segrià |
E317936
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Benavent de Segrià
Benavent de Segrià is a small municipality in the comarca of Segrià in Catalonia, northeastern Spain.
|
E1064421
|
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: Benavent de Segrià | Statement: [Segrià, hasMunicipality, Benavent de Segrià]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benavent de Segrià Context triple: [Segrià, hasMunicipality, Benavent de Segrià]
-
A.
Segarra
Segarra is a historical inland comarca in Catalonia, Spain, known for its rolling cereal plains, medieval castles, and the town of Cervera as its capital.
-
B.
Ampurias
Ampurias (Empúries) was an ancient Greek and later Roman coastal settlement in northeastern Spain that became an important trading hub in the western Mediterranean.
-
C.
Gandesa
Gandesa is a historic town in Catalonia, Spain, known for its wine production and role in the Battle of the Ebro during the Spanish Civil War.
-
D.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
E.
Arenys de Munt
Arenys de Munt is a municipality in the Maresme comarca of Catalonia, Spain, known for its Mediterranean setting and involvement in early Catalan independence referendums.
- 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: Benavent de Segrià Triple: [Segrià, hasMunicipality, Benavent de Segrià]
Generated description
Benavent de Segrià is a small municipality in the comarca of Segrià in Catalonia, northeastern Spain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Benavent de Segrià Target entity description: Benavent de Segrià is a small municipality in the comarca of Segrià in Catalonia, northeastern Spain.
-
A.
Segarra
Segarra is a historical inland comarca in Catalonia, Spain, known for its rolling cereal plains, medieval castles, and the town of Cervera as its capital.
-
B.
Ampurias
Ampurias (Empúries) was an ancient Greek and later Roman coastal settlement in northeastern Spain that became an important trading hub in the western Mediterranean.
-
C.
Gandesa
Gandesa is a historic town in Catalonia, Spain, known for its wine production and role in the Battle of the Ebro during the Spanish Civil War.
-
D.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
E.
Arenys de Munt
Arenys de Munt is a municipality in the Maresme comarca of Catalonia, Spain, known for its Mediterranean setting and involvement in early Catalan independence referendums.
- 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_69f7b8c288c08190af46fe7d114df338 |
completed | May 3, 2026, 9:06 p.m. |
| NEDg | Description generation | batch_69f7ba6558fc819082dae55863a9a3b1 |
completed | May 3, 2026, 9:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7bb15d31c81909959e50219c4d905 |
completed | May 3, 2026, 9:16 p.m. |
Created at: April 9, 2026, 9:31 p.m.