Triple

T3351127
Position Surface form Disambiguated ID Type / Status
Subject Frits Bolkestein E70494 entity
Predicate familyName P18 FINISHED
Object Bolkestein
Bolkestein is a Dutch surname most notably associated with Frits Bolkestein, a prominent Dutch politician and former European Commissioner.
 E351550 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: Bolkestein | Statement: [Frits Bolkestein, familyName, Bolkestein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bolkestein
Context triple: [Frits Bolkestein, familyName, Bolkestein]
  • A. Rocard
    Rocard is a French surname most notably associated with Michel Rocard, a former Prime Minister of France and prominent Socialist politician.
  • B. Gsell
    Gsell is a surname of Germanic origin borne by various notable individuals, including artists, scholars, and public figures.
  • C. Sitra
    Sitra is a small island in Bahrain known for its residential communities, industrial facilities, and role in the country’s oil and gas infrastructure.
  • D. Pontecorvo
    Pontecorvo is a historic town in central Italy’s Lazio region, known for its medieval architecture and strategic position along the Liri River.
  • E. Arvato
    Arvato is a global business process outsourcing and services provider specializing in customer relationship management, supply chain management, and digital solutions.
  • 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: Bolkestein
Triple: [Frits Bolkestein, familyName, Bolkestein]
Generated description
Bolkestein is a Dutch surname most notably associated with Frits Bolkestein, a prominent Dutch politician and former European Commissioner.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bolkestein
Target entity description: Bolkestein is a Dutch surname most notably associated with Frits Bolkestein, a prominent Dutch politician and former European Commissioner.
  • A. Rocard
    Rocard is a French surname most notably associated with Michel Rocard, a former Prime Minister of France and prominent Socialist politician.
  • B. Gsell
    Gsell is a surname of Germanic origin borne by various notable individuals, including artists, scholars, and public figures.
  • C. Sitra
    Sitra is a small island in Bahrain known for its residential communities, industrial facilities, and role in the country’s oil and gas infrastructure.
  • D. Pontecorvo
    Pontecorvo is a historic town in central Italy’s Lazio region, known for its medieval architecture and strategic position along the Liri River.
  • E. Arvato
    Arvato is a global business process outsourcing and services provider specializing in customer relationship management, supply chain management, and digital solutions.
  • 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_69ad85a4ef7c8190a29e2bbd6fa454e4 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb220721c81909eb4d8d35c923927 completed March 8, 2026, 5:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3252f3f248190bb61db5c4fd2781a completed March 12, 2026, 8:42 p.m.
NEDg Description generation batch_69b3290aa6dc8190bd45ec83094fc23e completed March 12, 2026, 8:58 p.m.
NED2 Entity disambiguation (via description) batch_69b32987fdb48190983babe4515c68b4 completed March 12, 2026, 9 p.m.
Created at: March 8, 2026, 3:12 p.m.