Triple

T10274940
Position Surface form Disambiguated ID Type / Status
Subject Mineta Transportation Institute E240937 entity
Predicate abbreviation P43 FINISHED
Object MTI
MTI is a research and education institute focused on improving surface transportation policy, management, and safety, based at San José State University.
 E851900 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: MTI | Statement: [Mineta Transportation Institute, abbreviation, MTI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MTI
Context triple: [Mineta Transportation Institute, abbreviation, MTI]
  • A. MFTI
    MFTI is a leading Russian university renowned for its rigorous training and research in physics, mathematics, and related technical sciences.
  • B. NMTI
    NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
  • C. NMTI
    NMTI is an acronym whose specific meaning depends on context, commonly referring to various technical or institutional names.
  • D. MTE
    MTE is a French company known for manufacturing the BB 7200 class of electric locomotives for the French railways.
  • E. MITEI
    MITEI is the Massachusetts Institute of Technology’s multidisciplinary research and education hub focused on advancing energy technologies, policy, and innovation for a low-carbon future.
  • 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: MTI
Triple: [Mineta Transportation Institute, abbreviation, MTI]
Generated description
MTI is a research and education institute focused on improving surface transportation policy, management, and safety, based at San José State University.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MTI
Target entity description: MTI is a research and education institute focused on improving surface transportation policy, management, and safety, based at San José State University.
  • A. MFTI
    MFTI is a leading Russian university renowned for its rigorous training and research in physics, mathematics, and related technical sciences.
  • B. NMTI
    NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
  • C. NMTI
    NMTI is an acronym whose specific meaning depends on context, commonly referring to various technical or institutional names.
  • D. MTE
    MTE is a French company known for manufacturing the BB 7200 class of electric locomotives for the French railways.
  • E. MITEI
    MITEI is the Massachusetts Institute of Technology’s multidisciplinary research and education hub focused on advancing energy technologies, policy, and innovation for a low-carbon future.
  • 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_69d381a94c1881908fc38fc263d9b9c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d28a9c508190824867c04e8dcbe7 completed April 7, 2026, 9:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f81b39008190af48de31de03682e completed April 9, 2026, 12:51 a.m.
NEDg Description generation batch_69d6fcad625881909304201c1ebb3bcb completed April 9, 2026, 1:11 a.m.
NED2 Entity disambiguation (via description) batch_69d6fd84cd708190816d94417294b52a completed April 9, 2026, 1:14 a.m.
Created at: April 6, 2026, 11:37 a.m.