Triple

T10491512
Position Surface form Disambiguated ID Type / Status
Subject Puyo E247429 entity
Predicate roadConnectedTo P11435 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: [Puyo, roadConnectedTo, Macas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Macas
Context triple: [Puyo, roadConnectedTo, 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. 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.
  • E. Bacong
    Bacong is a coastal municipality in the province of Negros Oriental in the Philippines, known for its historic church and proximity to Dumaguete City.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5097e1c888190bc8e039f2e46181e completed April 7, 2026, 1:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69d8dca37b0881908ced885d9853bc1b completed April 10, 2026, 11:18 a.m.
Created at: April 6, 2026, 12:24 p.m.