Triple

T6736487
Position Surface form Disambiguated ID Type / Status
Subject TU Dortmund University E153767 entity
Predicate shortName P43 FINISHED
Object TUDO
TUDO is the commonly used abbreviation for TU Dortmund University, a technical university in Dortmund, Germany known for its engineering, natural sciences, and computer science programs.
 E614959 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: TUDO | Statement: [TU Dortmund University, shortName, TUDO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TUDO
Context triple: [TU Dortmund University, shortName, TUDO]
  • A. ALL
    ALL is the stock ticker symbol for Allstate Corporation, a major U.S. insurance company known primarily for its auto and home insurance products.
  • B. Tabuaço
    Tabuaço is a Portuguese municipality in the Douro region, known for its terraced vineyards and production of Port and Douro wines.
  • C. TOP
    TOP is the IATA airport code for Philip Billard Municipal Airport serving Topeka, Kansas, in the United States.
  • D. TTO
    TTO is a DARPA office focused on developing and demonstrating high-risk, high-payoff advanced military technologies and systems.
  • E. TTO
    TTO is the FIFA country code representing the Trinidad and Tobago national football team in international competitions.
  • 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: TUDO
Triple: [TU Dortmund University, shortName, TUDO]
Generated description
TUDO is the commonly used abbreviation for TU Dortmund University, a technical university in Dortmund, Germany known for its engineering, natural sciences, and computer science programs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TUDO
Target entity description: TUDO is the commonly used abbreviation for TU Dortmund University, a technical university in Dortmund, Germany known for its engineering, natural sciences, and computer science programs.
  • A. ALL
    ALL is the stock ticker symbol for Allstate Corporation, a major U.S. insurance company known primarily for its auto and home insurance products.
  • B. Tabuaço
    Tabuaço is a Portuguese municipality in the Douro region, known for its terraced vineyards and production of Port and Douro wines.
  • C. TOP
    TOP is the IATA airport code for Philip Billard Municipal Airport serving Topeka, Kansas, in the United States.
  • D. TTO
    TTO is a DARPA office focused on developing and demonstrating high-risk, high-payoff advanced military technologies and systems.
  • E. TTO
    TTO is the FIFA country code representing the Trinidad and Tobago national football team in international competitions.
  • 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_69c6880bdd68819097de8b6099992682 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d18369d88190a73349075462202b completed March 27, 2026, 6:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69c70b09b97c8190a5a538571b6909f0 completed March 27, 2026, 10:56 p.m.
NEDg Description generation batch_69c70bda97f08190bc6dab7177341876 completed March 27, 2026, 10:59 p.m.
NED2 Entity disambiguation (via description) batch_69c70c51e0148190be64afb56690b34f completed March 27, 2026, 11:01 p.m.
Created at: March 27, 2026, 2:09 p.m.