Triple

T11292100
Position Surface form Disambiguated ID Type / Status
Subject Aulnay-sous-Bois E267348 entity
Predicate hasTwinTown P919 FINISHED
Object Geleen
Geleen is a town in the Dutch province of Limburg known historically for its coal mining industry and later as part of the chemical and industrial hub around Sittard-Geleen.
 E1221732 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: Geleen | Statement: [Aulnay-sous-Bois, hasTwinTown, Geleen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Geleen
Context triple: [Aulnay-sous-Bois, hasTwinTown, Geleen]
  • A. Deurne
    Deurne is a district of the Belgian city of Antwerp, known for its residential neighborhoods and green spaces such as Rivierenhof park.
  • B. Deurne
    Deurne is a municipality in the Dutch province of North Brabant, known for its rural character and historic peat extraction areas.
  • C. Breukelen
    Breukelen is a Dutch town in the province of Utrecht, known as the namesake of Brooklyn in New York City.
  • D. Eeklo
    Eeklo is a small city and municipality in northwestern Belgium known for its historic town center and location in the Flemish region.
  • E. Coevorden
    Coevorden is a historic city and municipality in the northeastern Netherlands, known for its medieval fortress layout and strategic location near the German border.
  • 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: Geleen
Triple: [Aulnay-sous-Bois, hasTwinTown, Geleen]
Generated description
Geleen is a town in the Dutch province of Limburg known historically for its coal mining industry and later as part of the chemical and industrial hub around Sittard-Geleen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Geleen
Target entity description: Geleen is a town in the Dutch province of Limburg known historically for its coal mining industry and later as part of the chemical and industrial hub around Sittard-Geleen.
  • A. Deurne
    Deurne is a district of the Belgian city of Antwerp, known for its residential neighborhoods and green spaces such as Rivierenhof park.
  • B. Deurne
    Deurne is a municipality in the Dutch province of North Brabant, known for its rural character and historic peat extraction areas.
  • C. Breukelen
    Breukelen is a Dutch town in the province of Utrecht, known as the namesake of Brooklyn in New York City.
  • D. Eeklo
    Eeklo is a small city and municipality in northwestern Belgium known for its historic town center and location in the Flemish region.
  • E. Coevorden
    Coevorden is a historic city and municipality in the northeastern Netherlands, known for its medieval fortress layout and strategic location near the German border.
  • 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_69d6aac993a08190a6f36445ebaf9a43 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e989fdac81909a4a75f1f68b55c6 completed April 9, 2026, 6:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a006ec7a4748190822e66a756bc95b9 completed May 10, 2026, 11:40 a.m.
NEDg Description generation batch_6a006fa02870819083c1b25eb4c8ffad completed May 10, 2026, 11:44 a.m.
NED2 Entity disambiguation (via description) batch_6a0070aee0248190b3463b98a739d1ae completed May 10, 2026, 11:49 a.m.
Created at: April 8, 2026, 9:32 p.m.