Triple

T2353128
Position Surface form Disambiguated ID Type / Status
Subject Royal College of Music in Stockholm E47492 entity
Predicate shortName P43 FINISHED
Object KMH
KMH is the Royal College of Music in Stockholm, a leading Swedish institution for higher education in music performance, composition, and pedagogy.
 E257630 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: KMH | Statement: [Royal College of Music in Stockholm, shortName, KMH]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KMH
Context triple: [Royal College of Music in Stockholm, shortName, KMH]
  • A. KRH
    KRH is the commonly used abbreviation for the King’s Royal Hussars, a British Army cavalry regiment.
  • B. KMK
    KMK is the central coordinating body of Germany’s state education and cultural ministers, responsible for harmonizing policies across the federal states.
  • C. KCH
    KCH is the vehicle registration code assigned to the town of Chrzanów in southern Poland.
  • D. KGH
    KGH is the National Rail station code for Kinghorn railway station in Fife, Scotland.
  • E. TKM
    TKM is the three-letter ISO 3166-1 alpha-3 country code assigned to Turkmenistan.
  • 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: KMH
Triple: [Royal College of Music in Stockholm, shortName, KMH]
Generated description
KMH is the Royal College of Music in Stockholm, a leading Swedish institution for higher education in music performance, composition, and pedagogy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KMH
Target entity description: KMH is the Royal College of Music in Stockholm, a leading Swedish institution for higher education in music performance, composition, and pedagogy.
  • A. KRH
    KRH is the commonly used abbreviation for the King’s Royal Hussars, a British Army cavalry regiment.
  • B. KMK
    KMK is the central coordinating body of Germany’s state education and cultural ministers, responsible for harmonizing policies across the federal states.
  • C. KCH
    KCH is the vehicle registration code assigned to the town of Chrzanów in southern Poland.
  • D. KGH
    KGH is the National Rail station code for Kinghorn railway station in Fife, Scotland.
  • E. TKM
    TKM is the three-letter ISO 3166-1 alpha-3 country code assigned to Turkmenistan.
  • 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_69a88a1b678c8190bce986922ba60ce0 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc6fa6ecc8190821c9d5db341cf19 completed March 7, 2026, 6:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae9633ce3c81908581e7e0f8211ac1 completed March 9, 2026, 9:43 a.m.
NEDg Description generation batch_69ae971aa3bc8190b0b8b216106dca90 completed March 9, 2026, 9:47 a.m.
NED2 Entity disambiguation (via description) batch_69ae9795cf048190bc7a01ef86c12138 completed March 9, 2026, 9:49 a.m.
Created at: March 4, 2026, 7:54 p.m.