Triple

T148835
Position Surface form Disambiguated ID Type / Status
Subject His Majesty’s Government E3387 entity
Predicate alsoKnownAs P39 FINISHED
Object HMG
HMG is the common abbreviation for His Majesty’s Government, the central executive authority of the United Kingdom responsible for national policy and administration.
 E18364 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: HMG | Statement: [His Majesty’s Government, alsoKnownAs, HMG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HMG
Context triple: [His Majesty’s Government, alsoKnownAs, HMG]
  • A. HMT
    HMT is the commonly used abbreviation for HM Treasury, the United Kingdom government department responsible for economic and financial policy.
  • B. HARV
    HARV is the standard abbreviation used for the Harvard Crimson men's basketball team in collegiate athletics contexts.
  • C. HCR
    HCR is the commonly used abbreviation for the United Nations High Commissioner for Refugees, the UN agency responsible for protecting and supporting refugees worldwide.
  • D. HUP
    HUP is the commonly used abbreviation for Harvard University Press, a major academic publishing house affiliated with Harvard University.
  • E. HUP
    HUP is a major academic medical center in Philadelphia that serves as the flagship teaching hospital of the University of Pennsylvania's health system.
  • 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: HMG
Triple: [His Majesty’s Government, alsoKnownAs, HMG]
Generated description
HMG is the common abbreviation for His Majesty’s Government, the central executive authority of the United Kingdom responsible for national policy and administration.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HMG
Target entity description: HMG is the common abbreviation for His Majesty’s Government, the central executive authority of the United Kingdom responsible for national policy and administration.
  • A. HMT
    HMT is the commonly used abbreviation for HM Treasury, the United Kingdom government department responsible for economic and financial policy.
  • B. HARV
    HARV is the standard abbreviation used for the Harvard Crimson men's basketball team in collegiate athletics contexts.
  • C. HCR
    HCR is the commonly used abbreviation for the United Nations High Commissioner for Refugees, the UN agency responsible for protecting and supporting refugees worldwide.
  • D. HUP
    HUP is a major academic medical center in Philadelphia that serves as the flagship teaching hospital of the University of Pennsylvania's health system.
  • E. HUP
    HUP is the commonly used abbreviation for Harvard University Press, a major academic publishing house affiliated with Harvard University.
  • 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_69a252868de4819080e21c9938bfe8b6 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a257ecb6f48190992c4c8ca908a81c completed Feb. 28, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2c939ea148190984dfd347b12d136 completed Feb. 28, 2026, 10:53 a.m.
NEDg Description generation batch_69a2c99aa5348190a710ede093b2d1d7 completed Feb. 28, 2026, 10:55 a.m.
NED2 Entity disambiguation (via description) batch_69a2c9f070788190aa96892ffdc58569 completed Feb. 28, 2026, 10:56 a.m.
Created at: Feb. 28, 2026, 2:31 a.m.