Triple

T6785877
Position Surface form Disambiguated ID Type / Status
Subject La Nueva Isabela E155800 entity
Predicate locatedOn P40 FINISHED
Object Ozama River E208157 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: Ozama River | Statement: [La Nueva Isabela, locatedOn, Ozama River]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ozama River
Context triple: [La Nueva Isabela, locatedOn, Ozama River]
  • A. Ozama River chosen
    The Ozama River is a major waterway in the Dominican Republic that flows through the capital city of Santo Domingo and into the Caribbean Sea.
  • B. Oya River
    The Oya River is a river in Sarawak, Malaysia, that flows through coastal lowlands and supports local communities with fishing, transport, and agriculture.
  • C. Sei River
    The Sei River is a smaller watercourse in western India that feeds into the Sabarmati River within the arid and semi-arid landscape of Rajasthan and Gujarat.
  • D. Okano River
    The Okano River is a significant river in Gabon that serves as one of the principal tributaries feeding the Ogooué River system.
  • E. Segama River
    The Segama River is one of the longest and most significant rivers in the Malaysian state of Sabah, known for flowing through rich rainforest and wildlife habitats in eastern Borneo.
  • 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_69c6881770fc8190972b2906390380f5 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d28f043081909a9a9ab635785933 completed March 27, 2026, 6:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7881214488190914a3ee08fce359e completed March 28, 2026, 7:49 a.m.
Created at: March 27, 2026, 2:14 p.m.