Triple

T10294592
Position Surface form Disambiguated ID Type / Status
Subject Coastal Region of Ecuador E241449 entity
Predicate includesCity P3207 FINISHED
Object Manta E241444 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: Manta | Statement: [Coastal Region of Ecuador, includesCity, Manta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manta
Context triple: [Coastal Region of Ecuador, includesCity, Manta]
  • A. Manta
    Manta is a municipality located in Almeidas Province in the Cundinamarca Department of central Colombia.
  • B. Manta chosen
    Manta is a major coastal city and important seaport in western Ecuador, known for its fishing industry, beaches, and commercial activity.
  • C. Manta
    Manta is a flying roller coaster at SeaWorld Orlando that simulates the graceful, gliding motion of a manta ray through a combination of high-speed thrills and aquatic theming.
  • D. Manta
    Manta is a distributed object storage and compute service designed for running parallel computations directly on stored data in the cloud.
  • E. Manta Rota
    Manta Rota is a coastal village and popular beach resort in Portugal’s Algarve region, known for its long sandy beach and calm, shallow waters.
  • 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_69d381aaafc08190af475ef58dc16aba completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d2d5e0f88190be3e23ba2511a1e9 completed April 7, 2026, 9:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69d75018383481909abbba8247a93f8e completed April 9, 2026, 7:07 a.m.
Created at: April 6, 2026, 11:42 a.m.