Triple

T11160341
Position Surface form Disambiguated ID Type / Status
Subject Lycoming T53 E264017 entity
Predicate notableVariant P4680 FINISHED
Object T53-L-1
The T53-L-1 is an early production variant of the Lycoming T53 turboshaft engine, used primarily to power military helicopters.
 E909197 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: T53-L-1 | Statement: [Lycoming T53, notableVariant, T53-L-1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: T53-L-1
Context triple: [Lycoming T53, notableVariant, T53-L-1]
  • A. T5
    T5 is one of the lines of the Athens tram system, providing light-rail transit service along part of the city’s coastal and urban corridor.
  • B. T5
    T5 is a tram line of the Trambesòs light rail network serving the Barcelona metropolitan area.
  • C. T5
    T5 is a major passenger terminal at London Heathrow Airport, primarily serving British Airways and Iberia flights.
  • D. T5
    T5 is a former passenger terminal of Berlin Brandenburg Airport that handled commercial air traffic before being closed to operations.
  • E. T5
    T5 is a Transformer-based text-to-text language model developed by Google that treats every NLP task as converting input text to output text.
  • 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: T53-L-1
Triple: [Lycoming T53, notableVariant, T53-L-1]
Generated description
The T53-L-1 is an early production variant of the Lycoming T53 turboshaft engine, used primarily to power military helicopters.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: T53-L-1
Target entity description: The T53-L-1 is an early production variant of the Lycoming T53 turboshaft engine, used primarily to power military helicopters.
  • A. T5
    T5 is one of the lines of the Athens tram system, providing light-rail transit service along part of the city’s coastal and urban corridor.
  • B. T5
    T5 is a tram line of the Trambesòs light rail network serving the Barcelona metropolitan area.
  • C. T5
    T5 is a major passenger terminal at London Heathrow Airport, primarily serving British Airways and Iberia flights.
  • D. T5
    T5 is a former passenger terminal of Berlin Brandenburg Airport that handled commercial air traffic before being closed to operations.
  • E. T5
    T5 is a Transformer-based text-to-text language model developed by Google that treats every NLP task as converting input text to output text.
  • 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_69d6aa9ccddc8190868998c8b7beb060 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8817a90819087820d5241c58851 completed April 9, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4636268d0819088873f2062115506 completed April 19, 2026, 5:08 a.m.
NEDg Description generation batch_69e46c374ca08190a876ee68dea9b821 completed April 19, 2026, 5:46 a.m.
NED2 Entity disambiguation (via description) batch_69e4747bc02c81908f0782cf85667f3f completed April 19, 2026, 6:21 a.m.
Created at: April 8, 2026, 9:29 p.m.