Triple

T9470487
Position Surface form Disambiguated ID Type / Status
Subject Deutsches Architekturmuseum E228375 entity
Predicate shortName P43 FINISHED
Object DAM
DAM is a Frankfurt-based museum dedicated to the history, theory, and contemporary practice of architecture in Germany and beyond.
 E800646 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: DAM | Statement: [Deutsches Architekturmuseum, shortName, DAM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DAM
Context triple: [Deutsches Architekturmuseum, shortName, DAM]
  • A. DAM
    DAM is the three-letter IATA airport code for Damascus International Airport, the main airport serving Syria’s capital city.
  • B. DAM
    DAM is the National Rail station code used to identify Dalmeny railway station in Scotland’s rail network.
  • C. DPM
    DPM is the abbreviation for the División de Policía Militar, a military police division responsible for law enforcement and security duties within a nation's armed forces.
  • D. DIT
    DIT is the South Australian government department responsible for planning, developing, and managing the state’s transport systems and infrastructure.
  • E. DAS
    DAS is the acronym for the Defense Attache Service, the U.S. military organization that manages defense attachés and military diplomatic representation at American embassies worldwide.
  • 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: DAM
Triple: [Deutsches Architekturmuseum, shortName, DAM]
Generated description
DAM is a Frankfurt-based museum dedicated to the history, theory, and contemporary practice of architecture in Germany and beyond.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DAM
Target entity description: DAM is a Frankfurt-based museum dedicated to the history, theory, and contemporary practice of architecture in Germany and beyond.
  • A. DAM
    DAM is the three-letter IATA airport code for Damascus International Airport, the main airport serving Syria’s capital city.
  • B. DAM
    DAM is the National Rail station code used to identify Dalmeny railway station in Scotland’s rail network.
  • C. DPM
    DPM is the abbreviation for the División de Policía Militar, a military police division responsible for law enforcement and security duties within a nation's armed forces.
  • D. DIT
    DIT is the South Australian government department responsible for planning, developing, and managing the state’s transport systems and infrastructure.
  • E. DAS
    DAS is the acronym for the Defense Attache Service, the U.S. military organization that manages defense attachés and military diplomatic representation at American embassies worldwide.
  • 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_69ca847162c48190b079076c9595513c completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd7fee13a88190b4532fb92ddaf401 completed April 1, 2026, 8:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69d122c45ae88190a43d70300bed98da completed April 4, 2026, 2:40 p.m.
NEDg Description generation batch_69d1240e67988190bc16da2c9087775d completed April 4, 2026, 2:45 p.m.
NED2 Entity disambiguation (via description) batch_69d1246979d88190890e2ccf0fdf63cf completed April 4, 2026, 2:47 p.m.
Created at: March 30, 2026, 7:53 p.m.