Triple

T14324638
Position Surface form Disambiguated ID Type / Status
Subject AFAS Circustheater E355183 entity
Predicate hasSponsor P35686 FINISHED
Object AFAS Software E956319 NE FINISHED

How this triple was built (2 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: AFAS Software | Statement: [AFAS Circustheater, hasSponsor, AFAS Software]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AFAS Software
Context triple: [AFAS Circustheater, hasSponsor, AFAS Software]
  • A. AFAS Software chosen
    AFAS Software is a Dutch company that develops business and accounting software solutions for organizations in various sectors.
  • B. AFAS
    AFAS is a regional agreement among ASEAN member states aimed at progressively liberalizing trade in services to enhance economic integration and competitiveness in Southeast Asia.
  • C. AFAS Live
    AFAS Live is a major indoor music and events venue in Amsterdam, Netherlands, known for hosting concerts, shows, and large-scale entertainment performances.
  • D. Argotec
    Argotec is an Italian aerospace engineering company specializing in the design and development of small satellites and space systems for scientific and commercial missions.
  • E. Amadeus IT Group
    Amadeus IT Group is a leading global travel technology company that provides reservation, distribution, and IT solutions for airlines, travel agencies, hotels, and other travel industry players.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d8278fa2108190bc0d0e7939c1eb03 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de883e6a288190b6c22f630a1eef3c completed April 14, 2026, 6:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd468e263c81909d7261bcfd949579 completed May 8, 2026, 2:12 a.m.
Created at: April 10, 2026, 1:13 a.m.