Triple

T11132329
Position Surface form Disambiguated ID Type / Status
Subject Ekeren E263314 entity
Predicate hasNeighbouringDistrict P17964 FINISHED
Object Merksem E172479 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: Merksem | Statement: [Ekeren, hasNeighbouringDistrict, Merksem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Merksem
Context triple: [Ekeren, hasNeighbouringDistrict, Merksem]
  • A. Merksem chosen
    Merksem is a northern district of the Belgian city of Antwerp, known as a predominantly residential area with local commerce and sports facilities.
  • B. Mereč
    Mereč is a locality whose name appears in various transliterated forms, including "Merecz" and "Mereč," reflecting linguistic and historical variations in the region.
  • C. Saksun
    Saksun is a small, picturesque village in the Faroe Islands known for its turf-roofed houses, surrounding mountains, and a lagoon formed from a natural harbor.
  • D. Merkens
    Merkens is a German surname most notably associated with Olympic track cyclist Toni Merkens.
  • E. Melle
    Melle is a town in Lower Saxony, Germany, known for its rural character, historical architecture, and role as a regional economic center.
  • 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_69d6aa9c0ba08190bbd19c217489b755 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8347a248190837e8c26f25f553a completed April 9, 2026, 5:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69e441e6b72881908f8288e99df0cb7c completed April 19, 2026, 2:45 a.m.
Created at: April 8, 2026, 9:28 p.m.