Triple

T1031402
Position Surface form Disambiguated ID Type / Status
Subject Evan Williams E22258 entity
Predicate employer P7 FINISHED
Object Medium E95216 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: Medium | Statement: [Evan Williams, employer, Medium]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Medium
Context triple: [Evan Williams, employer, Medium]
  • A. Medium chosen
    Medium is an online publishing platform that allows writers and readers to share and discover long-form articles, essays, and stories across a wide range of topics.
  • B. Medium Cool
    Medium Cool is a 1969 American drama film directed by Haskell Wexler that blends fiction and documentary techniques to depict social and political unrest surrounding the 1968 Democratic National Convention in Chicago.
  • C. Min
    Min is a common given name of Chinese origin used for both males and females.
  • D. Mitte
    Mitte is the central district of Berlin, Germany, known as the historic core of the city and home to many major landmarks and government institutions.
  • E. Middelaar
    Middelaar is a village in the Dutch province of Limburg, situated near the river Maas and close to the border with Germany.
  • 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_69a493d848848190aed4011b34b2e8d3 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b810429081908a97014ca740824b completed March 1, 2026, 10:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac429675bc8190b2467ac86c41c3b5 completed March 7, 2026, 3:21 p.m.
Created at: March 1, 2026, 7:41 p.m.