Triple

T2361234
Position Surface form Disambiguated ID Type / Status
Subject Norway, Maine E47275 entity
Predicate adjacentTo P224 FINISHED
Object Greenwood, Maine E529215 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: Greenwood, Maine | Statement: [Norway, Maine, adjacentTo, Greenwood, Maine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Greenwood, Maine
Context triple: [Norway, Maine, adjacentTo, Greenwood, Maine]
  • A. Greenwood, Maine chosen
    Greenwood, Maine is a small rural town in Oxford County known for its scenic lakes, forests, and outdoor recreation in western Maine.
  • B. Greenfield, Maine
    Greenfield, Maine is a small rural town located in Penobscot County in the central part of the state.
  • C. Greene, Maine
    Greene, Maine is a small rural town in Androscoggin County known for its quiet residential character and proximity to the Lewiston–Auburn area.
  • D. Garland, Maine
    Garland, Maine is a small rural town located in Penobscot County in central Maine, known for its quiet countryside and close-knit community.
  • E. Woodville, Maine
    Woodville, Maine is a small rural town located in Penobscot County in the central part of the state.
  • 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_69a88a1a4a6081908645b0f2914521ab completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc723c66481908a9b94991f651b3b completed March 7, 2026, 6:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69c757f5a2f4819081f5bde51e30641a completed March 28, 2026, 4:24 a.m.
Created at: March 4, 2026, 7:55 p.m.