Triple

T20511895
Position Surface form Disambiguated ID Type / Status
Subject GM High Feature V6 engines E503584 entity
Predicate familyMember P566 FINISHED
Object LLT
LLT is a 3.6-liter direct-injected V6 gasoline engine from General Motors known for its use in various mid- to late-2000s GM vehicles, offering a balance of performance and efficiency.
 E1435479 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: LLT | Statement: [GM High Feature V6 engines, familyMember, LLT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LLT
Context triple: [GM High Feature V6 engines, familyMember, LLT]
  • A. LTT
    LTT is the IATA airport code for Golfe de Saint-Tropez Airport, a regional airport serving the Saint-Tropez area in southeastern France.
  • B. LLR
    LLR is the ICAO airline designator assigned to Alliance Air, an Indian regional airline.
  • C. HLLT
    HLLT is the ICAO airport code for Tripoli International Airport, the main international gateway serving Tripoli, Libya.
  • D. LLL
    LLL is a low-level, Lisp-like programming language used to write smart contracts that compile to Ethereum Virtual Machine bytecode.
  • E. LLO
    LLO is the National Rail station code for Llandrindod railway station in Powys, Wales.
  • 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: LLT
Triple: [GM High Feature V6 engines, familyMember, LLT]
Generated description
LLT is a 3.6-liter direct-injected V6 gasoline engine from General Motors known for its use in various mid- to late-2000s GM vehicles, offering a balance of performance and efficiency.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LLT
Target entity description: LLT is a 3.6-liter direct-injected V6 gasoline engine from General Motors known for its use in various mid- to late-2000s GM vehicles, offering a balance of performance and efficiency.
  • A. LTT
    LTT is the IATA airport code for Golfe de Saint-Tropez Airport, a regional airport serving the Saint-Tropez area in southeastern France.
  • B. LLR
    LLR is the ICAO airline designator assigned to Alliance Air, an Indian regional airline.
  • C. HLLT
    HLLT is the ICAO airport code for Tripoli International Airport, the main international gateway serving Tripoli, Libya.
  • D. LLL
    LLL is a low-level, Lisp-like programming language used to write smart contracts that compile to Ethereum Virtual Machine bytecode.
  • E. LLO
    LLO is the National Rail station code for Llandrindod railway station in Powys, Wales.
  • 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_69e0b4b2aa788190ae9eb37c1d73b1f1 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e69dcb8f5c8190b0d4c09f3669a8ec completed April 20, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a089d36f4688190a084267f85744cc0 completed May 16, 2026, 4:37 p.m.
NEDg Description generation batch_6a089de26d548190882d6685aaa7f5fa completed May 16, 2026, 4:40 p.m.
NED2 Entity disambiguation (via description) batch_6a089e5c696881909d1d793b884464a1 completed May 16, 2026, 4:42 p.m.
Created at: April 16, 2026, 11:36 a.m.