Triple

T1374689
Position Surface form Disambiguated ID Type / Status
Subject Sacramento Valley E30192 entity
Predicate contains P35 FINISHED
Object City of Woodland E115395 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: City of Woodland | Statement: [Sacramento Valley, contains, City of Woodland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Woodland
Context triple: [Sacramento Valley, contains, City of Woodland]
  • A. City of Woodland chosen
    The City of Woodland is a small agricultural and residential city in California’s Sacramento Valley that serves as the county seat and commercial hub of Yolo County.
  • B. Springwood
    Springwood is a major town in the Lower Blue Mountains region of New South Wales, Australia, known as a residential and commercial hub amid bushland and scenic surroundings.
  • C. Springwood
    Springwood is the historic Hudson River estate in Hyde Park, New York, best known as the lifelong home and presidential library site of Franklin D. Roosevelt.
  • D. Oakwood
    Oakwood is a residential neighborhood on Staten Island, New York City, known for its suburban character and proximity to the island’s eastern shore.
  • E. City of Lakes
    City of Lakes is a popular nickname for Minneapolis, highlighting its many urban lakes and waterfronts.
  • 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_69a498f912008190a376a98b207b2071 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c2f7aeb08190b52ef1058c18327e completed March 1, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad08a9dd308190999d349f8b6297b8 completed March 8, 2026, 5:27 a.m.
Created at: March 1, 2026, 7:57 p.m.