Triple

T3220160
Position Surface form Disambiguated ID Type / Status
Subject Tivoli Gardens E67492 entity
Predicate ownedBy P347 FINISHED
Object Tivoli A/S E338217 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: Tivoli A/S | Statement: [Tivoli Gardens, ownedBy, Tivoli A/S]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tivoli A/S
Context triple: [Tivoli Gardens, ownedBy, Tivoli A/S]
  • A. Tivoli A/S chosen
    Tivoli A/S is the Danish company that owns and manages Copenhagen’s historic Tivoli Gardens amusement park and related entertainment and hospitality operations.
  • B. Marcussen & Søn
    Marcussen & Søn is a renowned Danish firm celebrated for crafting high-quality pipe organs for churches and concert halls worldwide.
  • C. Intamin
    Intamin is a Swiss-based company renowned worldwide for designing and manufacturing major amusement rides and roller coasters for theme parks.
  • D. Hitachi
    Hitachi is a Japanese multinational conglomerate known for its wide range of businesses spanning information technology, infrastructure, industrial systems, and consumer electronics.
  • E. Mitsubishi Electric
    Mitsubishi Electric is a global Japanese electronics and electrical equipment manufacturer known for producing advanced technologies ranging from factory automation systems and power equipment to large-scale display and video board solutions.
  • 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_69ad858b8adc8190ad989712c87a476b completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adae16f20081909d7f3bac016f961d completed March 8, 2026, 5:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2771be134819081809b2351517fdf completed March 12, 2026, 8:19 a.m.
Created at: March 8, 2026, 3:08 p.m.