Triple

T1229422
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Humanities, University of Oslo E26401 entity
Predicate shortName P43 FINISHED
Object HF
HF is the Faculty of Humanities at the University of Oslo, encompassing disciplines such as languages, history, culture, and philosophy.
 E140419 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: HF | Statement: [Faculty of Humanities, University of Oslo, shortName, HF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HF
Context triple: [Faculty of Humanities, University of Oslo, shortName, HF]
  • A. HN
    HN is the two-letter ISO 3166-1 alpha-2 country code assigned to Honduras.
  • B. 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.
  • C. HAV
    HAV is the IATA airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • D. HM
    HM is an abbreviation commonly used as a formal title for a reigning queen or king, standing for "Her Majesty" or "His Majesty."
  • E. BF
    BF is the IATA airline designator for French Bee, a French low-cost long-haul carrier.
  • 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: HF
Triple: [Faculty of Humanities, University of Oslo, shortName, HF]
Generated description
HF is the Faculty of Humanities at the University of Oslo, encompassing disciplines such as languages, history, culture, and philosophy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HF
Target entity description: HF is the Faculty of Humanities at the University of Oslo, encompassing disciplines such as languages, history, culture, and philosophy.
  • A. HN
    HN is the two-letter ISO 3166-1 alpha-2 country code assigned to Honduras.
  • B. 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.
  • C. HAV
    HAV is the IATA airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • D. HM
    HM is an abbreviation commonly used as a formal title for a reigning queen or king, standing for "Her Majesty" or "His Majesty."
  • E. BF
    BF is the IATA airline designator for French Bee, a French low-cost long-haul carrier.
  • 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_69a4948571c88190a9191e451e6035fd completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4be3dac2c8190914ff27173bb6b34 completed March 1, 2026, 10:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac8a1242048190ba6ffcaacc4ca5d5 completed March 7, 2026, 8:26 p.m.
NEDg Description generation batch_69ac8a9d03c8819097cea548a31d866d completed March 7, 2026, 8:29 p.m.
NED2 Entity disambiguation (via description) batch_69ac8b041e188190ac9e1ce2c2728c94 completed March 7, 2026, 8:31 p.m.
Created at: March 1, 2026, 7:47 p.m.