Triple

T8937964
Position Surface form Disambiguated ID Type / Status
Subject Berlin-Lichtenberg E212823 entity
Predicate containsLocality P45140 FINISHED
Object Falkenberg
Falkenberg is a locality in the borough of Lichtenberg in Berlin, Germany, known for its more rural character on the city's northeastern edge.
 E769403 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: Falkenberg | Statement: [Berlin-Lichtenberg, containsLocality, Falkenberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Falkenberg
Context triple: [Berlin-Lichtenberg, containsLocality, Falkenberg]
  • A. Falkenberg
    Falkenberg is a coastal town in southwestern Sweden known for its beaches, fishing heritage, and location along the River Ätran.
  • B. Falköping
    Falköping is a small Swedish town known for its surrounding ancient burial mounds, rolling agricultural landscape, and location between the plateaus of Mösseberg and Ålleberg.
  • C. Fredrikshamn
    Fredrikshamn (Hamina) is a coastal town in southeastern Finland that historically served as an important military and trading center.
  • D. Oskarshamn
    Oskarshamn is a coastal town in southeastern Sweden known for its Baltic Sea harbor and proximity to the island of Gotland.
  • E. Fagerborg
    Fagerborg is a residential neighborhood in Oslo, Norway, known for its central location, historic buildings, and proximity to major educational institutions.
  • 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: Falkenberg
Triple: [Berlin-Lichtenberg, containsLocality, Falkenberg]
Generated description
Falkenberg is a locality in the borough of Lichtenberg in Berlin, Germany, known for its more rural character on the city's northeastern edge.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Falkenberg
Target entity description: Falkenberg is a locality in the borough of Lichtenberg in Berlin, Germany, known for its more rural character on the city's northeastern edge.
  • A. Falkenberg
    Falkenberg is a coastal town in southwestern Sweden known for its beaches, fishing heritage, and location along the River Ätran.
  • B. Falköping
    Falköping is a small Swedish town known for its surrounding ancient burial mounds, rolling agricultural landscape, and location between the plateaus of Mösseberg and Ålleberg.
  • C. Fredrikshamn
    Fredrikshamn (Hamina) is a coastal town in southeastern Finland that historically served as an important military and trading center.
  • D. Oskarshamn
    Oskarshamn is a coastal town in southeastern Sweden known for its Baltic Sea harbor and proximity to the island of Gotland.
  • E. Fagerborg
    Fagerborg is a residential neighborhood in Oslo, Norway, known for its central location, historic buildings, and proximity to major educational institutions.
  • 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_69ca839694c88190b324ffeb43d23b08 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc66b57a348190979effe4f9998eb7 completed April 1, 2026, 12:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc937ded08190a67fd4457b6458ff completed April 3, 2026, 2:05 p.m.
NEDg Description generation batch_69cfc9b8cd108190a9b3482858b0363a completed April 3, 2026, 2:07 p.m.
NED2 Entity disambiguation (via description) batch_69cfca54d4dc8190a2f94a6f3d2acaf9 completed April 3, 2026, 2:10 p.m.
Created at: March 30, 2026, 6:58 p.m.