Triple
T10047191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portalegre District |
E207644
|
entity |
| Predicate | hasBorderTown |
P847
|
FINISHED |
| Object | Campo Maior |
E832970
|
NE FINISHED |
How this triple was built (2 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: Campo Maior | Statement: [Portalegre District, hasBorderTown, Campo Maior]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Campo Maior Context triple: [Portalegre District, hasBorderTown, Campo Maior]
-
A.
Campo Maior
Campo Maior is a municipality in the Brazilian state of Piauí, known historically for its role in regional conflicts and its cultural traditions.
-
B.
Campo Maior
chosen
Campo Maior is a historic town in Portugal’s Alentejo region, known for its coffee industry and colorful Flower Festival.
-
C.
Morrinhos
Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
-
D.
Laranjal Paulista
Laranjal Paulista is a municipality in the state of São Paulo, Brazil, known for its riverside setting and regional agricultural activities.
-
E.
Campo Grande
Campo Grande is a neighborhood in the city of Recife, Brazil.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca835ad0608190b7c80b292da004f5 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcf664dd881908786fcd802bf10da |
completed | April 2, 2026, 2:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d29a4064a48190b4fdb6bf3ea5af05 |
completed | April 5, 2026, 5:22 p.m. |
Created at: March 30, 2026, 8:56 p.m.