Triple

T7600802
Position Surface form Disambiguated ID Type / Status
Subject Kenneth Arrow E179975 entity
Predicate doctoralStudent P167 FINISHED
Object Michael Spence E665196 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 Spence | Statement: [Kenneth Arrow, doctoralStudent, Michael Spence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Spence
Context triple: [Kenneth Arrow, doctoralStudent, Michael Spence]
  • A. Michael Spence
    Michael Spence is an Australian legal scholar and university leader who has served as the head of major universities, including University College London.
  • B. Michael Spence chosen
    Michael Spence is a Nobel Prize–winning American economist best known for his work on signaling theory in markets with asymmetric information.
  • C. Karl E. Case
    Karl E. Case was an American economist best known for co-developing the widely used Case-Shiller Home Price Index that tracks U.S. residential real estate prices.
  • D. Joseph Stiglitz
    Joseph Stiglitz is a Nobel Prize–winning American economist renowned for his work on information asymmetry, inequality, and critiques of unregulated markets.
  • E. Dale T. Mortensen
    Dale T. Mortensen was an American economist and Nobel laureate renowned for his pioneering work on search and matching theory in labor economics.
  • 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_69c69f3567008190ab01d2ca7b53584a completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f9d9c55c8190841f3bf3225c096a completed March 27, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c86846192c81909154e6cf60b21157 completed March 28, 2026, 11:46 p.m.
Created at: March 27, 2026, 3:53 p.m.