Triple

T13187037
Position Surface form Disambiguated ID Type / Status
Subject Konedobu E313882 entity
Predicate nearbyArea P2064 FINISHED
Object Boroko E313879 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: Boroko | Statement: [Konedobu, nearbyArea, Boroko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Boroko
Context triple: [Konedobu, nearbyArea, Boroko]
  • A. Boroko chosen
    Boroko is a major residential and commercial suburb of Port Moresby in Papua New Guinea, known for its shopping areas and sports facilities.
  • B. Asokoro
    Asokoro is an upscale residential and administrative district in Abuja, Nigeria, known for hosting many government institutions, embassies, and high-profile residents.
  • C. Basoko
    Basoko is a riverside town in the Democratic Republic of the Congo, situated at the confluence of the Aruwimi and Congo Rivers and serving as a local administrative and trading center.
  • D. Bompoka
    Bompoka is a small, remote island that forms part of India’s Nicobar Islands archipelago in the eastern Indian Ocean.
  • E. Tokoro
    Tokoro is a coastal district of Kitami City in Hokkaido, Japan, known historically for its fishing industry and drift ice along the Sea of Okhotsk.
  • 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_69d806ae1e08819090d95bfe1538cc17 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c4b663c8190b0b18f0785f7b57d completed April 10, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f70a2e415481908ad1036376f702dc completed May 3, 2026, 8:41 a.m.
Created at: April 9, 2026, 9:15 p.m.