Triple
T5719424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abrantes |
E126104
|
entity |
| Predicate | hasParish |
P35
|
FINISHED |
| Object |
Bemposta
Bemposta is a civil parish located within the municipality of Abrantes in central Portugal.
|
E540296
|
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: Bemposta | Statement: [Abrantes, hasParish, Bemposta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bemposta Context triple: [Abrantes, hasParish, Bemposta]
-
A.
Pampilhosa da Serra
Pampilhosa da Serra is a small municipality in central Portugal known for its mountainous landscapes, schist villages, and forested river valleys.
-
B.
Pau dos Ferros
Pau dos Ferros is a municipality in the interior of Brazil’s Rio Grande do Norte state, known as a regional commercial and educational hub in the Alto Oeste Potiguar region.
-
C.
Neiva
Neiva is a major city in southwestern Colombia known as the economic and cultural center of the upper Magdalena River valley.
-
D.
Limeira
Limeira is a municipality in the interior of the Brazilian state of São Paulo, known for its industrial activity and history in the jewelry and citrus sectors.
-
E.
Cantanhede
Cantanhede is a Portuguese municipality in the Centro Region known for its wine production, agricultural activity, and proximity to the Atlantic coast.
- 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: Bemposta Triple: [Abrantes, hasParish, Bemposta]
Generated description
Bemposta is a civil parish located within the municipality of Abrantes in central Portugal.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bemposta Target entity description: Bemposta is a civil parish located within the municipality of Abrantes in central Portugal.
-
A.
Pampilhosa da Serra
Pampilhosa da Serra is a small municipality in central Portugal known for its mountainous landscapes, schist villages, and forested river valleys.
-
B.
Pau dos Ferros
Pau dos Ferros is a municipality in the interior of Brazil’s Rio Grande do Norte state, known as a regional commercial and educational hub in the Alto Oeste Potiguar region.
-
C.
Neiva
Neiva is a major city in southwestern Colombia known as the economic and cultural center of the upper Magdalena River valley.
-
D.
Limeira
Limeira is a municipality in the interior of the Brazilian state of São Paulo, known for its industrial activity and history in the jewelry and citrus sectors.
-
E.
Cantanhede
Cantanhede is a Portuguese municipality in the Centro Region known for its wine production, agricultural activity, and proximity to the Atlantic coast.
- 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_69c0082e3d548190950169847b43043b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c024e1ec7c8190a08e1b7954db2a9d |
completed | March 22, 2026, 5:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a7db0788190b4a5e7b5d9c94588 |
completed | March 22, 2026, 9:09 p.m. |
| NEDg | Description generation | batch_69c05b7c3bd48190ad8303bf1bb3ec6a |
completed | March 22, 2026, 9:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c05c22c31081909a9a67d99e7c728c |
completed | March 22, 2026, 9:16 p.m. |
Created at: March 22, 2026, 3:46 p.m.