Triple

T418538
Position Surface form Disambiguated ID Type / Status
Subject Bureau of Labor Statistics E8047 entity
Predicate abbreviation P43 FINISHED
Object BLS
BLS is the principal U.S. federal agency that collects, analyzes, and disseminates essential economic data on labor markets, prices, and productivity.
 E52961 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: BLS | Statement: [Bureau of Labor Statistics, abbreviation, BLS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BLS
Context triple: [Bureau of Labor Statistics, abbreviation, BLS]
  • A. BOL
    BOL is the three-letter ISO 3166-1 alpha-3 country code assigned to Bolivia.
  • B. BEL
    BEL is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies Belgium in international standards and data systems.
  • C. BSC
    BSC is an acronym commonly referring to British Security Coordination, a covert World War II intelligence organization established by the United Kingdom in North America.
  • D. BAL
    BAL is the Amtrak station code for Pennsylvania Station in Baltimore, Maryland, a major rail hub in the city’s transportation network.
  • E. BLI
    BLI is the commonly used abbreviation for the OECD Better Life Index, an interactive tool that compares well-being across countries using multiple quality-of-life indicators.
  • 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: BLS
Triple: [Bureau of Labor Statistics, abbreviation, BLS]
Generated description
BLS is the principal U.S. federal agency that collects, analyzes, and disseminates essential economic data on labor markets, prices, and productivity.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BLS
Target entity description: BLS is the principal U.S. federal agency that collects, analyzes, and disseminates essential economic data on labor markets, prices, and productivity.
  • A. BOL
    BOL is the three-letter ISO 3166-1 alpha-3 country code assigned to Bolivia.
  • B. BEL
    BEL is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies Belgium in international standards and data systems.
  • C. BSC
    BSC is an acronym commonly referring to British Security Coordination, a covert World War II intelligence organization established by the United Kingdom in North America.
  • D. BAL
    BAL is the Amtrak station code for Pennsylvania Station in Baltimore, Maryland, a major rail hub in the city’s transportation network.
  • E. BLI
    BLI is the commonly used abbreviation for the OECD Better Life Index, an interactive tool that compares well-being across countries using multiple quality-of-life indicators.
  • 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_69a2e7f1d1bc81909cf2dc9754a3c334 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ee917de48190965fba455efd2320 completed Feb. 28, 2026, 1:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69a423a4debc819098e13855b550a72e completed March 1, 2026, 11:31 a.m.
NEDg Description generation batch_69a42418a28c81909ee31dfb1819b87f completed March 1, 2026, 11:33 a.m.
NED2 Entity disambiguation (via description) batch_69a42488226c81908c9c81567fed0efa completed March 1, 2026, 11:35 a.m.
Created at: Feb. 28, 2026, 1:11 p.m.