Triple

T933596
Position Surface form Disambiguated ID Type / Status
Subject Gro Harlem Brundtland E20147 entity
Predicate givenName P17 FINISHED
Object Gro
Gro is the given name of Gro Harlem Brundtland, the Norwegian physician and politician who served three terms as Prime Minister of Norway and later led the World Health Organization.
 E109389 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: Gro | Statement: [Gro Harlem Brundtland, givenName, Gro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gro
Context triple: [Gro Harlem Brundtland, givenName, Gro]
  • A. GU
    GU is the two-letter ISO 3166 country code assigned to Guam, an unincorporated territory of the United States in the western Pacific Ocean.
  • B. Ga
    Ga is a Kwa language spoken primarily by the Ga people in and around Accra, the capital region of Ghana.
  • C. GD
    GD is the vehicle registration code used on license plates for cars registered in the city of Gdańsk, Poland.
  • D. GN
    GN is a fast, meta-build system tool used primarily by the Chromium project to generate build files for Ninja.
  • E. GER
    GER is the official FIFA country code used to represent the Germany national football team in international competitions and records.
  • 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: Gro
Triple: [Gro Harlem Brundtland, givenName, Gro]
Generated description
Gro is the given name of Gro Harlem Brundtland, the Norwegian physician and politician who served three terms as Prime Minister of Norway and later led the World Health Organization.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gro
Target entity description: Gro is the given name of Gro Harlem Brundtland, the Norwegian physician and politician who served three terms as Prime Minister of Norway and later led the World Health Organization.
  • A. GU
    GU is the two-letter ISO 3166 country code assigned to Guam, an unincorporated territory of the United States in the western Pacific Ocean.
  • B. Ga
    Ga is a Kwa language spoken primarily by the Ga people in and around Accra, the capital region of Ghana.
  • C. GD
    GD is the vehicle registration code used on license plates for cars registered in the city of Gdańsk, Poland.
  • D. GN
    GN is a fast, meta-build system tool used primarily by the Chromium project to generate build files for Ninja.
  • E. GER
    GER is the official FIFA country code used to represent the Germany national football team in international competitions and records.
  • 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_69a493af3dc48190adb7263e6e445ea1 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b3627ccc8190a836515b2ea85ec5 completed March 1, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7ee12da388190a26f0f7944d6f5f8 completed March 4, 2026, 8:32 a.m.
NEDg Description generation batch_69a7f12e48f88190bd0aac156a76f0b9 completed March 4, 2026, 8:45 a.m.
NED2 Entity disambiguation (via description) batch_69a7f1a2688881908524f10350137f4f completed March 4, 2026, 8:47 a.m.
Created at: March 1, 2026, 7:40 p.m.