Triple

T565210
Position Surface form Disambiguated ID Type / Status
Subject South Holland E13536 entity
Predicate hostsInstitution P186 FINISHED
Object Europol E36588 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: Europol | Statement: [South Holland, hostsInstitution, Europol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Europol
Context triple: [South Holland, hostsInstitution, Europol]
  • A. Europol chosen
    Europol is the European Union’s law enforcement agency that supports member states in combating serious international crime and terrorism.
  • B. Europaeum
    Europaeum is a network of leading European universities dedicated to promoting academic collaboration, European studies, and cross-border dialogue in higher education.
  • C. Hollandia
    Hollandia is an engineering firm known for its role in designing and constructing major structures such as the London Eye.
  • D. Friesland
    Friesland is a northern province of the Netherlands known for its distinct Frisian language, rich maritime history, and unique cultural traditions.
  • E. Eure
    Eure is a river in northern France that flows through the regions of Normandy and Centre-Val de Loire before joining the Seine.
  • 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_69a4933edcf08190b35ecfd6014caee6 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49a74793481908fee3baff0b1d348 completed March 1, 2026, 7:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69a501bdb7cc8190922432fe58cfd6cb completed March 2, 2026, 3:19 a.m.
Created at: March 1, 2026, 7:32 p.m.