Triple
T13012915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Upano River |
E322467
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Macas |
E263067
|
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: Macas | Statement: [Upano River, nearCity, Macas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Macas Context triple: [Upano River, nearCity, Macas]
-
A.
Macas
chosen
Macas is a city in southeastern Ecuador that serves as an administrative and commercial hub in the Amazonian region.
-
B.
Gumaca
Gumaca is a coastal municipality in the province of Quezon in the Philippines, known for its historic churches and role as a local commercial center.
-
C.
Mamanguape
Mamanguape is a municipality in the Brazilian state of Paraíba, known for its historical colonial architecture and location near the Mamanguape River on the state’s northern coast.
-
D.
Allacapan
Allacapan is a rural municipality in the province of Cagayan in the Cagayan Valley region of the Philippines.
-
E.
Pinangat
Pinangat is a traditional Filipino dish from the Bicol Region made of taro leaves, coconut milk, and chilies, known for its rich, spicy flavor.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecbb8f4819094d55eb07cb5ad97 |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c11290e08190a41c162d47094203 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:49 p.m.