Triple
T5958079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zêzere River |
E132565
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Belmonte |
E374153
|
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: Belmonte | Statement: [Zêzere River, flowsThrough, Belmonte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belmonte Context triple: [Zêzere River, flowsThrough, Belmonte]
-
A.
Belmonte
chosen
Belmonte is a historic town in Portugal known for its medieval castle and strong Jewish heritage, located in the country's Centro Region.
-
B.
Montalva
Montalva is a Spanish-language surname notably associated with Chilean president Eduardo Frei Montalva.
-
C.
Moncalvo
Moncalvo is a small historic town in Italy’s Piedmont region, known as one of the country’s smallest cities and for its wine and truffle production.
-
D.
Bombarral
Bombarral is a small Portuguese town in the Oeste subregion known for its wine production and agricultural landscape.
-
E.
Avellaneda
Avellaneda is a city in the Buenos Aires Province of Argentina, known as an important industrial and port center within the Greater Buenos Aires metropolitan area.
- 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_69c0086b05cc8190a8f36a96927a525c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c039c48d0c81908e794c52fddf2ca2 |
completed | March 22, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0e3e3736c8190b445156f0c1bdf1f |
completed | March 23, 2026, 6:55 a.m. |
Created at: March 22, 2026, 4:02 p.m.