Triple

T20079202
Position Surface form Disambiguated ID Type / Status
Subject Will Hurley E499952 entity
Predicate alsoKnownAs P39 FINISHED
Object whurley
whurley is an American entrepreneur and technologist best known for founding multiple startups in areas like open source software and quantum computing, including Strangeworks.
 E1410622 NE FINISHED

How this triple was built (4 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: whurley | Statement: [Will Hurley, alsoKnownAs, whurley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: whurley
Context triple: [Will Hurley, alsoKnownAs, whurley]
  • A. WHE
    WHE is the National Rail station code for Whalley railway station in Lancashire, England.
  • B. WRH
    WRH is the National Rail station code for Worthing railway station in West Sussex, England.
  • C. WRY
    WRY is the IATA airport code for Westray Airport, a small regional airport in Orkney, Scotland known for operating one of the world’s shortest scheduled flights.
  • D. HUR
    HUR is the station code used to identify Hurstville railway station in the Sydney Trains network.
  • E. Wreh
    Wreh is a surname most notably associated with Liberian footballer Christopher Wreh, who played as a striker for clubs including Arsenal and the Liberia national team.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: whurley
Triple: [Will Hurley, alsoKnownAs, whurley]
Generated description
whurley is an American entrepreneur and technologist best known for founding multiple startups in areas like open source software and quantum computing, including Strangeworks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: whurley
Target entity description: whurley is an American entrepreneur and technologist best known for founding multiple startups in areas like open source software and quantum computing, including Strangeworks.
  • A. WHE
    WHE is the National Rail station code for Whalley railway station in Lancashire, England.
  • B. WRH
    WRH is the National Rail station code for Worthing railway station in West Sussex, England.
  • C. WRY
    WRY is the IATA airport code for Westray Airport, a small regional airport in Orkney, Scotland known for operating one of the world’s shortest scheduled flights.
  • D. HUR
    HUR is the station code used to identify Hurstville railway station in the Sydney Trains network.
  • E. Wreh
    Wreh is a surname most notably associated with Liberian footballer Christopher Wreh, who played as a striker for clubs including Arsenal and the Liberia national team.
  • F. None of above. chosen

Provenance (5 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_69da627770948190997f486f9a2e370f completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6643e216c819088c002fc1de2772a completed April 20, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a081f43b37c819098e55bab84433896 completed May 16, 2026, 7:39 a.m.
NEDg Description generation batch_6a0820057ee8819091d299de1559e1e2 completed May 16, 2026, 7:43 a.m.
NED2 Entity disambiguation (via description) batch_6a08208c203c819083abea34d10d5e4e completed May 16, 2026, 7:45 a.m.
Created at: April 11, 2026, 3:40 p.m.