Triple

T7803428
Position Surface form Disambiguated ID Type / Status
Subject German Historical Museum E180486 entity
Predicate hasAbbreviation P43 FINISHED
Object DHM
DHM is the commonly used abbreviation for the German Historical Museum in Berlin, a major institution dedicated to documenting and presenting German history.
 E695641 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: DHM | Statement: [German Historical Museum, hasAbbreviation, DHM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DHM
Context triple: [German Historical Museum, hasAbbreviation, DHM]
  • A. DHMİ
    DHMİ is Turkey’s state-owned General Directorate of State Airports Authority, responsible for operating and managing the country’s major airports and air navigation services.
  • B. DH
    DH is the commonly used currency symbol representing the Moroccan dirham.
  • C. DH
    DH is the postcode area covering Durham and surrounding parts of North East England in the United Kingdom.
  • D. DH
    DH is the official abbreviation for Puerto Rico’s Department of Treasury, the government agency responsible for managing the island’s fiscal and tax affairs.
  • E. DH
    DH is the regional vehicle registration code assigned to motor vehicles registered in Kupang, Indonesia.
  • 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: DHM
Triple: [German Historical Museum, hasAbbreviation, DHM]
Generated description
DHM is the commonly used abbreviation for the German Historical Museum in Berlin, a major institution dedicated to documenting and presenting German history.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DHM
Target entity description: DHM is the commonly used abbreviation for the German Historical Museum in Berlin, a major institution dedicated to documenting and presenting German history.
  • A. DHMİ
    DHMİ is Turkey’s state-owned General Directorate of State Airports Authority, responsible for operating and managing the country’s major airports and air navigation services.
  • B. DH
    DH is the postcode area covering Durham and surrounding parts of North East England in the United Kingdom.
  • C. DH
    DH is the commonly used currency symbol representing the Moroccan dirham.
  • D. DH
    DH is the official abbreviation for Puerto Rico’s Department of Treasury, the government agency responsible for managing the island’s fiscal and tax affairs.
  • E. DH
    DH is the regional vehicle registration code assigned to motor vehicles registered in Kupang, Indonesia.
  • 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_69ca827e50cc8190a92a733577184938 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69caf635a4648190af907a686d87f073 completed March 30, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb14371e2081908d0a798d3b785c3c completed March 31, 2026, 12:24 a.m.
NEDg Description generation batch_69cb1a1414a08190912945cee30e6bc2 completed March 31, 2026, 12:49 a.m.
NED2 Entity disambiguation (via description) batch_69cb3843368881908d7c4d5a81c53a23 completed March 31, 2026, 2:58 a.m.
Created at: March 30, 2026, 4:34 p.m.