Triple

T4368435
Position Surface form Disambiguated ID Type / Status
Subject Chalonnes-sur-Loire E98834 entity
Predicate twinnedWith P1072 FINISHED
Object Petershagen
Petershagen is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and scenic location along the Weser River.
E453633 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: Petershagen | Statement: [Chalonnes-sur-Loire, twinnedWith, Petershagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Petershagen
Context triple: [Chalonnes-sur-Loire, twinnedWith, Petershagen]
  • A. Sprockhövel
    Sprockhövel is a small town in North Rhine-Westphalia, Germany, known for its historical coal mining heritage and location in the hilly Ruhr region.
  • B. Schönewalde
    Schönewalde is a town in the state of Brandenburg, Germany, known for hosting a German Air Force base.
  • C. Schöneberg
    Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
  • D. Niederschönhausen
    Niederschönhausen is a residential district in the Berlin borough of Pankow, known for its historic villas, green spaces, and the former presidential residence Schloss Schönhausen.
  • E. Hasselwerder
    Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
  • 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: Petershagen
Triple: [Chalonnes-sur-Loire, twinnedWith, Petershagen]
Generated description
Petershagen is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and scenic location along the Weser River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Petershagen
Target entity description: Petershagen is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and scenic location along the Weser River.
  • A. Sprockhövel
    Sprockhövel is a small town in North Rhine-Westphalia, Germany, known for its historical coal mining heritage and location in the hilly Ruhr region.
  • B. Schönewalde
    Schönewalde is a town in the state of Brandenburg, Germany, known for hosting a German Air Force base.
  • C. Schöneberg
    Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
  • D. Niederschönhausen
    Niederschönhausen is a residential district in the Berlin borough of Pankow, known for its historic villas, green spaces, and the former presidential residence Schloss Schönhausen.
  • E. Hasselwerder
    Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
  • 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_69b3454db3708190aeafd814413c4c3d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b352034d3881909ed4b2f9eef5e823 completed March 12, 2026, 11:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdd36a7c9c81908f325bc8a53db0c8 completed March 20, 2026, 11:08 p.m.
NEDg Description generation batch_69bdd500b0088190abf6c7616a379f5c completed March 20, 2026, 11:15 p.m.
NED2 Entity disambiguation (via description) batch_69bdd563a7fc8190a7091add8ef0e717 completed March 20, 2026, 11:16 p.m.
Created at: March 12, 2026, 11:17 p.m.