Triple

T1176482
Position Surface form Disambiguated ID Type / Status
Subject Max Levchin E25036 entity
Predicate founded P104 FINISHED
Object HVF
HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
 E134693 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: HVF | Statement: [Max Levchin, founded, HVF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HVF
Context triple: [Max Levchin, founded, HVF]
  • A. HARV
    HARV is the standard abbreviation used for the Harvard Crimson men's basketball team in collegiate athletics contexts.
  • B. VIF
    VIF is the commonly used abbreviation and nickname for Vålerenga Fotball, a Norwegian professional football club based in Oslo.
  • C. THF
    THF is the former Berlin Tempelhof Airport, a historically significant airfield known for its role in the Berlin Airlift and its later conversion into a vast urban park.
  • D. HAV
    HAV is the IATA airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • E. YV
    YV is the IATA airline designator used to identify Mesa Airlines in flight schedules and ticketing systems.
  • 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: HVF
Triple: [Max Levchin, founded, HVF]
Generated description
HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HVF
Target entity description: HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
  • A. HARV
    HARV is the standard abbreviation used for the Harvard Crimson men's basketball team in collegiate athletics contexts.
  • B. VIF
    VIF is the commonly used abbreviation and nickname for Vålerenga Fotball, a Norwegian professional football club based in Oslo.
  • C. THF
    THF is the former Berlin Tempelhof Airport, a historically significant airfield known for its role in the Berlin Airlift and its later conversion into a vast urban park.
  • D. HAV
    HAV is the IATA airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • E. YV
    YV is the IATA airline designator used to identify Mesa Airlines in flight schedules and ticketing systems.
  • 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_69a494267b4c819088c97a59182bf56a completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bd0d5c288190b597dae0fbe3b43b completed March 1, 2026, 10:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac6f1cab308190bdb5ae1e01d83b61 completed March 7, 2026, 6:31 p.m.
NEDg Description generation batch_69ac6ff4c62881908cf88169e99d2983 completed March 7, 2026, 6:35 p.m.
NED2 Entity disambiguation (via description) batch_69ac704cdaf08190b77b0b9345537d84 completed March 7, 2026, 6:37 p.m.
Created at: March 1, 2026, 7:45 p.m.