Triple

T18253603
Position Surface form Disambiguated ID Type / Status
Subject Chris Urmson E437162 entity
Predicate employer P7 FINISHED
Object Aurora Innovation 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: Aurora Innovation | Statement: [Chris Urmson, employer, Aurora Innovation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aurora Innovation
Context triple: [Chris Urmson, employer, Aurora Innovation]
  • A. Aurora Innovation chosen
    Aurora Innovation is an autonomous vehicle technology company focused on developing self-driving systems for cars, trucks, and other vehicles.
  • B. Aurora Network
    Aurora Network is a European university alliance focused on collaboration in research, education, and innovation among its member institutions.
  • C. Innogen
    Innogen is the original name of Imogen, the virtuous and wronged heroine of William Shakespeare’s play "Cymbeline."
  • D. Innoventions
    Innoventions was an interactive exhibit pavilion at Epcot in Walt Disney World that showcased emerging technologies and hands-on science displays.
  • E. Innosight
    Innosight is a strategy and innovation consulting firm co-founded by disruptive innovation theorist Clayton Christensen, known for helping organizations navigate transformational growth and change.
  • 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_69d8b913351c8190932b6a426de04b41 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4fd82f81c81909ad4455954bd8caa completed April 19, 2026, 4:06 p.m.
Created at: April 10, 2026, 10:33 a.m.