Triple

T12238355
Position Surface form Disambiguated ID Type / Status
Subject Maine State Route 160 E291660 entity
Predicate passesThrough P225 FINISHED
Object Denmark, Maine E241984 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: Denmark, Maine | Statement: [Maine State Route 160, passesThrough, Denmark, Maine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Denmark, Maine
Context triple: [Maine State Route 160, passesThrough, Denmark, Maine]
  • A. Denmark, Maine chosen
    Denmark, Maine is a small rural town in Oxford County known for its lakeside setting in western Maine’s Lakes Region.
  • B. Norway, Maine
    Norway, Maine is a small New England town known for its historic downtown, lakes and outdoor recreation, located in western Maine.
  • C. Gray, Maine
    Gray, Maine is a small New England town in southern Maine known for its rural character, proximity to Portland, and the Maine Wildlife Park.
  • D. Sweden, Maine
    Sweden, Maine is a small rural town in Oxford County known for its scenic lakeside setting and outdoor recreation opportunities in western Maine.
  • E. Rockland, Maine
    Rockland, Maine is a coastal city in midcoast Maine known for its busy harbor, maritime industries, and role as a transportation and cultural hub for the region.
  • 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_69d6ab668acc8190963ba424049d6aee completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91cb45340819093365f8efdf85f75 completed April 10, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e5ff68c81909d2796b24dd055f4 completed May 2, 2026, 3:55 p.m.
Created at: April 8, 2026, 9:51 p.m.