Triple

T8589792
Position Surface form Disambiguated ID Type / Status
Subject MTU Friedrichshafen E203400 entity
Predicate brand P1500 FINISHED
Object MTU
MTU is a German brand best known for its high-performance diesel engines and propulsion systems used in marine, industrial, and power generation applications.
 E745146 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: MTU | Statement: [MTU Friedrichshafen, brand, MTU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MTU
Context triple: [MTU Friedrichshafen, brand, MTU]
  • A. MTU
    MTU is a public research university in Houghton, Michigan, known for its strong engineering, technology, and science programs.
  • B. MTC
    MTC is the station code for Meerut City railway station, a major rail hub in the city of Meerut, Uttar Pradesh, India.
  • C. MTC
    MTC is a regional planning and transportation agency that coordinates and funds transit, highways, and other mobility projects in the San Francisco Bay Area.
  • D. MTS
    MTS is a public transportation agency that operates bus and rail services in the San Diego metropolitan area of California.
  • E. MTS
    MTS is the three-letter National Rail station code for Montrose railway station in Angus, Scotland.
  • 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: MTU
Triple: [MTU Friedrichshafen, brand, MTU]
Generated description
MTU is a German brand best known for its high-performance diesel engines and propulsion systems used in marine, industrial, and power generation applications.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MTU
Target entity description: MTU is a German brand best known for its high-performance diesel engines and propulsion systems used in marine, industrial, and power generation applications.
  • A. MTU
    MTU is a public research university in Houghton, Michigan, known for its strong engineering, technology, and science programs.
  • B. MTC
    MTC is the station code for Meerut City railway station, a major rail hub in the city of Meerut, Uttar Pradesh, India.
  • C. MTC
    MTC is a regional planning and transportation agency that coordinates and funds transit, highways, and other mobility projects in the San Francisco Bay Area.
  • D. MTS
    MTS is a public transportation agency that operates bus and rail services in the San Diego metropolitan area of California.
  • E. MTS
    MTS is the three-letter National Rail station code for Montrose railway station in Angus, Scotland.
  • 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_69ca832a7f108190b4e4f5648abf4aa2 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc466471048190ad6351170d07f7f7 completed March 31, 2026, 10:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69cea8acebac81909d2fce98c6901f0c completed April 2, 2026, 5:34 p.m.
NEDg Description generation batch_69cea9cff1ec8190a0093fb42782341e completed April 2, 2026, 5:39 p.m.
NED2 Entity disambiguation (via description) batch_69ceaa9f7f8c8190965e86880ff141d5 completed April 2, 2026, 5:42 p.m.
Created at: March 30, 2026, 6:23 p.m.