Triple

T19943098
Position Surface form Disambiguated ID Type / Status
Subject Directorate of Technical Support and Emergency Management E479354 entity
Predicate abbreviation P43 FINISHED
Object DTSEM
DTSEM is a specialized division focused on providing technical support and coordinating emergency management and response activities.
 E1403198 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: DTSEM | Statement: [Directorate of Technical Support and Emergency Management, abbreviation, DTSEM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DTSEM
Context triple: [Directorate of Technical Support and Emergency Management, abbreviation, DTSEM]
  • A. DET
    DET is the standard NHL abbreviation for the Detroit Red Wings professional ice hockey team.
  • B. TAMSE
    TAMSE is an Argentine state-owned defense manufacturer best known for producing the TAM family of medium tanks and other armored vehicles.
  • C. TSE
    TSE is the primary stock exchange in Japan and one of the largest securities markets in the world, located in Tokyo.
  • D. DTL
    DTL is a type of linear accelerator structure that uses a series of drift tubes within an RF cavity to efficiently accelerate charged particle beams.
  • E. TDEM
    TDEM is the Texas state agency responsible for coordinating emergency management, disaster response, and preparedness efforts across the state.
  • 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: DTSEM
Triple: [Directorate of Technical Support and Emergency Management, abbreviation, DTSEM]
Generated description
DTSEM is a specialized division focused on providing technical support and coordinating emergency management and response activities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DTSEM
Target entity description: DTSEM is a specialized division focused on providing technical support and coordinating emergency management and response activities.
  • A. DET
    DET is the standard NHL abbreviation for the Detroit Red Wings professional ice hockey team.
  • B. TAMSE
    TAMSE is an Argentine state-owned defense manufacturer best known for producing the TAM family of medium tanks and other armored vehicles.
  • C. TSE
    TSE is the primary stock exchange in Japan and one of the largest securities markets in the world, located in Tokyo.
  • D. DTL
    DTL is a type of linear accelerator structure that uses a series of drift tubes within an RF cavity to efficiently accelerate charged particle beams.
  • E. TDEM
    TDEM is the Texas state agency responsible for coordinating emergency management, disaster response, and preparedness efforts across the state.
  • 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_69d8e522a17c819095165d4d24939fd8 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e65a63f2e48190ba1cb7a4f415e7f6 completed April 20, 2026, 4:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07f6f0c82081908dd0ba4110cec45e completed May 16, 2026, 4:47 a.m.
NEDg Description generation batch_6a07f97b5b94819087241ecc97e6583f completed May 16, 2026, 4:58 a.m.
NED2 Entity disambiguation (via description) batch_6a07f9dec2c48190bd18c2f24a87f335 completed May 16, 2026, 5 a.m.
Created at: April 10, 2026, 1:54 p.m.