Triple

T1066335
Position Surface form Disambiguated ID Type / Status
Subject Department for Innovation, Universities and Skills E23218 entity
Predicate shortName P43 FINISHED
Object DIUS
DIUS was a former UK government department responsible for higher education, innovation, and skills policy.
 E124035 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: DIUS | Statement: [Department for Innovation, Universities and Skills, shortName, DIUS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DIUS
Context triple: [Department for Innovation, Universities and Skills, shortName, DIUS]
  • A. Discaria
    Discaria is a small genus of spiny shrubs native to South America and New Zealand, known for their nitrogen-fixing ability and adaptation to dry, open habitats.
  • B. Veritas
    Veritas is the Latin word for "truth" and is famously used as the motto of Harvard University.
  • C. Siris
    Siris is a philosophical work by George Berkeley that explores metaphysics, theology, and the medicinal virtues of tar-water through a chain of reflective questions and arguments.
  • D. Stiris
    Stiris was an ancient Greek city in the region of Phocis, known as one of its notable urban centers in classical antiquity.
  • E. Degesch
    Degesch was a German chemical company best known for producing the pesticide Zyklon B, which was infamously used in Nazi extermination camps during the Holocaust.
  • 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: DIUS
Triple: [Department for Innovation, Universities and Skills, shortName, DIUS]
Generated description
DIUS was a former UK government department responsible for higher education, innovation, and skills policy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DIUS
Target entity description: DIUS was a former UK government department responsible for higher education, innovation, and skills policy.
  • A. Discaria
    Discaria is a small genus of spiny shrubs native to South America and New Zealand, known for their nitrogen-fixing ability and adaptation to dry, open habitats.
  • B. Veritas
    Veritas is the Latin word for "truth" and is famously used as the motto of Harvard University.
  • C. Siris
    Siris is a philosophical work by George Berkeley that explores metaphysics, theology, and the medicinal virtues of tar-water through a chain of reflective questions and arguments.
  • D. Stiris
    Stiris was an ancient Greek city in the region of Phocis, known as one of its notable urban centers in classical antiquity.
  • E. Degesch
    Degesch was a German chemical company best known for producing the pesticide Zyklon B, which was infamously used in Nazi extermination camps during the Holocaust.
  • 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_69a493ee1f908190992b5f0d1b04459b completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b910c6d481909c56f961a0e8720c completed March 1, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac42a336388190a6d18fda8a7a151d completed March 7, 2026, 3:22 p.m.
NEDg Description generation batch_69ac43f306e48190b94f16749f5ba0d1 completed March 7, 2026, 3:27 p.m.
NED2 Entity disambiguation (via description) batch_69ac44467c9c8190a82ea8158add468a completed March 7, 2026, 3:29 p.m.
Created at: March 1, 2026, 7:42 p.m.