Triple

T1264564
Position Surface form Disambiguated ID Type / Status
Subject Princeton Cemetery E12571 entity
Predicate containsGraveOf P3802 FINISHED
Object Michael Graves E130211 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: Michael Graves | Statement: [Princeton Cemetery, containsGraveOf, Michael Graves]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Graves
Context triple: [Princeton Cemetery, containsGraveOf, Michael Graves]
  • A. Michael Graves chosen
    Michael Graves was an influential American architect and designer renowned for his colorful, playful postmodern buildings and widely popular product designs.
  • B. Laurie Olin
    Laurie Olin is a prominent American landscape architect and urban designer known for shaping major public spaces in cities across the United States.
  • C. Thom Mayne
    Thom Mayne is an American architect and founder of the firm Morphosis, known for his bold, unconventional designs and influential role in contemporary architecture.
  • D. Peter Chermayeff
    Peter Chermayeff is an American architect renowned for designing major public aquariums around the world.
  • E. Richard Meier
    Richard Meier is an American architect renowned for his modernist, white geometric designs and major cultural projects such as the Getty Center in Los Angeles.
  • 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_69a4933352e08190ac617291985e76c0 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4c03573a48190b5851f0a734c2d6f completed March 1, 2026, 10:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad308179bc81909716b3acb9d59ea1 completed March 8, 2026, 8:17 a.m.
Created at: March 1, 2026, 7:50 p.m.