Triple

T15305100
Position Surface form Disambiguated ID Type / Status
Subject Hazardous Materials Transportation Act E365878 entity
Predicate shortName P43 FINISHED
Object HMTA
HMTA is a U.S. federal law that regulates the safe transportation of hazardous materials to protect people, property, and the environment.
 E1150594 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: HMTA | Statement: [Hazardous Materials Transportation Act, shortName, HMTA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HMTA
Context triple: [Hazardous Materials Transportation Act, shortName, HMTA]
  • A. HMT
    HMT is the commonly used abbreviation for HM Treasury, the United Kingdom government department responsible for economic and financial policy.
  • B. HMT
    HMT is the station code for Hamilton railway station in Scotland’s national rail network.
  • C. HMTM
    HMTM is a renowned German conservatory in Munich specializing in higher education for music and performing arts.
  • D. HMA
    HMA is the IATA airport code for Khanty-Mansiysk Airport, which serves the city of Khanty-Mansiysk in Russia.
  • E. HMA
    HMA is a European network of national medicines regulatory authorities that collaborates to ensure the quality, safety, and efficacy of medicinal products across member states.
  • 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: HMTA
Triple: [Hazardous Materials Transportation Act, shortName, HMTA]
Generated description
HMTA is a U.S. federal law that regulates the safe transportation of hazardous materials to protect people, property, and the environment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HMTA
Target entity description: HMTA is a U.S. federal law that regulates the safe transportation of hazardous materials to protect people, property, and the environment.
  • A. HMT
    HMT is the commonly used abbreviation for HM Treasury, the United Kingdom government department responsible for economic and financial policy.
  • B. HMT
    HMT is the station code for Hamilton railway station in Scotland’s national rail network.
  • C. HMTM
    HMTM is a renowned German conservatory in Munich specializing in higher education for music and performing arts.
  • D. HMA
    HMA is the IATA airport code for Khanty-Mansiysk Airport, which serves the city of Khanty-Mansiysk in Russia.
  • E. HMA
    HMA is a European network of national medicines regulatory authorities that collaborates to ensure the quality, safety, and efficacy of medicinal products across member states.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03ccef14c819099c5ebe962e7f867 completed April 16, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69fef89d961481909be8dcc2864982c9 completed May 9, 2026, 9:04 a.m.
NEDg Description generation batch_69fefa339f988190b470e052c853e4f8 completed May 9, 2026, 9:11 a.m.
NED2 Entity disambiguation (via description) batch_69fefac48df08190ad58e9d455546a57 completed May 9, 2026, 9:13 a.m.
Created at: April 10, 2026, 3:16 a.m.