Triple

T16067897
Position Surface form Disambiguated ID Type / Status
Subject Toyota Motor Manufacturing Texas E389778 entity
Predicate hasAbbreviation P43 FINISHED
Object TMMTX
TMMTX is Toyota’s truck manufacturing plant in San Antonio, Texas, known for producing vehicles like the Toyota Tundra and Tacoma.
 E1193320 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: TMMTX | Statement: [Toyota Motor Manufacturing Texas, hasAbbreviation, TMMTX]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TMMTX
Context triple: [Toyota Motor Manufacturing Texas, hasAbbreviation, TMMTX]
  • A. TMM
    TMM was the former currency code for the original Turkmenistan manat used before the country's monetary redenomination.
  • B. TMMK
    TMMK is Toyota’s large-scale automobile manufacturing plant in Georgetown, Kentucky, known for producing popular Toyota and Lexus models for the North American market.
  • C. MMTM
    MMTM is the ICAO airport code for General Francisco Javier Mina International Airport serving Tampico, Mexico.
  • D. TMO
    TMO is the stock ticker symbol for Thermo Fisher Scientific, a leading global provider of scientific instruments, reagents, and laboratory services.
  • E. TMB
    TMB is the commonly used abbreviation for the Technical Management Board, a governing body that oversees and coordinates technical and standardization activities within its organization.
  • 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: TMMTX
Triple: [Toyota Motor Manufacturing Texas, hasAbbreviation, TMMTX]
Generated description
TMMTX is Toyota’s truck manufacturing plant in San Antonio, Texas, known for producing vehicles like the Toyota Tundra and Tacoma.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TMMTX
Target entity description: TMMTX is Toyota’s truck manufacturing plant in San Antonio, Texas, known for producing vehicles like the Toyota Tundra and Tacoma.
  • A. TMM
    TMM was the former currency code for the original Turkmenistan manat used before the country's monetary redenomination.
  • B. TMMK
    TMMK is Toyota’s large-scale automobile manufacturing plant in Georgetown, Kentucky, known for producing popular Toyota and Lexus models for the North American market.
  • C. MMTM
    MMTM is the ICAO airport code for General Francisco Javier Mina International Airport serving Tampico, Mexico.
  • D. TMO
    TMO is the stock ticker symbol for Thermo Fisher Scientific, a leading global provider of scientific instruments, reagents, and laboratory services.
  • E. TMB
    TMB is the main public transportation operator in the Barcelona metropolitan area, managing the city’s metro and bus networks.
  • 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_69d86daf32ec8190a8c0466c8f49c3c0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e183baf29481908e5c66879e125363 completed April 17, 2026, 12:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffe480d59c8190962ac596a872b5e0 completed May 10, 2026, 1:50 a.m.
NEDg Description generation batch_69ffe6ee34788190942ef1d3bb805f78 completed May 10, 2026, 2:01 a.m.
NED2 Entity disambiguation (via description) batch_69ffe7d127848190bbd79a8b94a49f93 completed May 10, 2026, 2:05 a.m.
Created at: April 10, 2026, 4:57 a.m.