Triple

T3842361
Position Surface form Disambiguated ID Type / Status
Subject Pankow E93479 entity
Predicate contains P35 FINISHED
Object Blankenburg
Blankenburg is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and village-like atmosphere.
E545808 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: Blankenburg | Statement: [Pankow, contains, Blankenburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blankenburg
Context triple: [Pankow, contains, Blankenburg]
  • A. Warffum
    Warffum is a historic village in the Dutch province of Groningen, known for its traditional architecture and open-air museum showcasing rural life.
  • B. Sassenheim
    Sassenheim is a town in the Dutch province of South Holland, known historically for its bulb-growing industry and location within the Duin- en Bollenstreek (Dune and Bulb Region).
  • C. Nieuwenhoorn
    Nieuwenhoorn is a village in the western Netherlands that forms part of the province of South Holland.
  • D. Stadshagen
    Stadshagen is a residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
  • E. Papendrecht
    Papendrecht is a Dutch town situated on the river Merwede in the province of South Holland, known for its residential character and proximity to the city of Dordrecht.
  • 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: Blankenburg
Triple: [Pankow, contains, Blankenburg]
Generated description
Blankenburg is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and village-like atmosphere.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Blankenburg
Target entity description: Blankenburg is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and village-like atmosphere.
  • A. Warffum
    Warffum is a historic village in the Dutch province of Groningen, known for its traditional architecture and open-air museum showcasing rural life.
  • B. Sassenheim
    Sassenheim is a town in the Dutch province of South Holland, known historically for its bulb-growing industry and location within the Duin- en Bollenstreek (Dune and Bulb Region).
  • C. Nieuwenhoorn
    Nieuwenhoorn is a village in the western Netherlands that forms part of the province of South Holland.
  • D. Stadshagen
    Stadshagen is a residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
  • E. Papendrecht
    Papendrecht is a Dutch town situated on the river Merwede in the province of South Holland, known for its residential character and proximity to the city of Dordrecht.
  • 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_69aed96ce578819084ab16e3439976c9 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeebb397ac81908f74a42a0eeb8682 completed March 9, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07d33a65481908c7ab4473bed1320 completed March 22, 2026, 11:37 p.m.
NEDg Description generation batch_69c08c54755c819099decf3b6e4c6792 completed March 23, 2026, 12:41 a.m.
NED2 Entity disambiguation (via description) batch_69c08cc71a9c8190ac3aa082cb7bf0fc completed March 23, 2026, 12:43 a.m.
Created at: March 9, 2026, 3:18 p.m.