Triple

T4096416
Position Surface form Disambiguated ID Type / Status
Subject Alabama Law Enforcement Agency E87831 entity
Predicate shortName P43 FINISHED
Object ALEA
ALEA is the statewide law enforcement and public safety agency for the U.S. state of Alabama.
 E413505 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: ALEA | Statement: [Alabama Law Enforcement Agency, shortName, ALEA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ALEA
Context triple: [Alabama Law Enforcement Agency, shortName, ALEA]
  • A. ALLEA
    ALLEA (All European Academies) is a European federation that brings together national academies of sciences and humanities to promote science, scholarship, and evidence-based policy across Europe.
  • B. Ale
    Ale is a common short form of the Italian given name Alessandro, often used as a casual or affectionate nickname.
  • C. EALA
    EALA is the regional legislative body of the East African Community responsible for making laws and providing oversight for the bloc.
  • D. ALLA
    ALLA is a professional organization within anthropology that focuses on the scholarship, advocacy, and interests of Latina and Latino communities.
  • E. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • 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: ALEA
Triple: [Alabama Law Enforcement Agency, shortName, ALEA]
Generated description
ALEA is the statewide law enforcement and public safety agency for the U.S. state of Alabama.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ALEA
Target entity description: ALEA is the statewide law enforcement and public safety agency for the U.S. state of Alabama.
  • A. ALLEA
    ALLEA (All European Academies) is a European federation that brings together national academies of sciences and humanities to promote science, scholarship, and evidence-based policy across Europe.
  • B. Ale
    Ale is a common short form of the Italian given name Alessandro, often used as a casual or affectionate nickname.
  • C. EALA
    EALA is the regional legislative body of the East African Community responsible for making laws and providing oversight for the bloc.
  • D. ALLA
    ALLA is a professional organization within anthropology that focuses on the scholarship, advocacy, and interests of Latina and Latino communities.
  • E. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • 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_69aed94564cc8190a9c1457daedb6e7f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefcdef11081908c626a89f2c0e121 completed March 9, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b6f8bb081908aa2d126fe3c9502 completed March 14, 2026, 2:06 p.m.
NEDg Description generation batch_69b56f3cf1d081908e2fb778433fb2e8 completed March 14, 2026, 2:22 p.m.
NED2 Entity disambiguation (via description) batch_69b56f99d490819093f92b4db63c5375 completed March 14, 2026, 2:24 p.m.
Created at: March 9, 2026, 3:40 p.m.