Triple

T13783995
Position Surface form Disambiguated ID Type / Status
Subject Cross Insurance Center E331205 entity
Predicate serves P98 FINISHED
Object Bangor metropolitan area E7564 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: Bangor metropolitan area | Statement: [Cross Insurance Center, serves, Bangor metropolitan area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bangor metropolitan area
Context triple: [Cross Insurance Center, serves, Bangor metropolitan area]
  • A. Bangor metropolitan area chosen
    The Bangor metropolitan area is a regional urban and economic hub in central-eastern Maine centered on the city of Bangor and its surrounding communities.
  • B. Bangor
    Bangor is a coastal town in Northern Ireland known for its marina, seaside resort heritage, and role as a commuter hub for nearby Belfast.
  • C. Bangor
    Bangor is a historic cathedral city in northwest Wales, known for its university and scenic location near the Menai Strait.
  • D. Bangor
    Bangor is a coastal commune located on Belle-Île, an island off the coast of Brittany in northwestern France.
  • E. City of Bangor
    The City of Bangor is a municipality in Maine that serves as a regional economic, cultural, and service hub for the surrounding area.
  • 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_69d81c58feb08190a77bca8bf7d6d20f completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0247ccc881908dad7b547221f15d completed April 14, 2026, 9 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c0e3642481908a9b84d8d71fb4d4 completed May 3, 2026, 9:40 p.m.
Created at: April 9, 2026, 10:11 p.m.