Triple

T23501618
Position Surface form Disambiguated ID Type / Status
Subject San Isabel, Colorado E571860 entity
Predicate hasNearbyWaterBody P1489 FINISHED
Object Lake Isabel NE NERFINISHED

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: Lake Isabel | Statement: [San Isabel, Colorado, hasNearbyWaterBody, Lake Isabel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Isabel
Context triple: [San Isabel, Colorado, hasNearbyWaterBody, Lake Isabel]
  • A. Lake Isabel chosen
    Lake Isabel is a scenic mountain reservoir and recreation area in southern Colorado known for fishing, hiking, and camping.
  • B. Lake Alpine
    Lake Alpine is a scenic high-elevation reservoir in the Sierra Nevada known for fishing, boating, and outdoor recreation.
  • C. June Lake
    June Lake is a scenic alpine lake and resort community in California’s Eastern Sierra Nevada, known for fishing, hiking, and skiing.
  • D. Clark Lake
    Clark Lake is a recreational inland lake in Jackson County, Michigan, known for boating, fishing, and lakeside cottages.
  • E. Boulder Lake
    Boulder Lake is a scenic mountain lake known for its surrounding wilderness and opportunities for hiking, fishing, and camping.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245b4829881909b77a70e942bbd54 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a8fd65e08190ae94ad4f0638cc0e completed April 29, 2026, 6:45 a.m.
Created at: April 17, 2026, 6:06 p.m.