Triple

T1039269
Position Surface form Disambiguated ID Type / Status
Subject Economic Research Service E22434 entity
Predicate abbreviation P43 FINISHED
Object ERS
ERS is the principal economic and social science research agency of the U.S. Department of Agriculture, providing data and analysis on agriculture, food, the environment, and rural development.
 E120165 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: ERS | Statement: [Economic Research Service, abbreviation, ERS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ERS
Context triple: [Economic Research Service, abbreviation, ERS]
  • A. ER
    ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago emergency room.
  • B. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • C. ER
    ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
  • D. REN
    REN is a blockchain-based project and protocol focused on enabling cross-chain liquidity and interoperability between different cryptocurrency networks.
  • E. RE
    RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
  • 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: ERS
Triple: [Economic Research Service, abbreviation, ERS]
Generated description
ERS is the principal economic and social science research agency of the U.S. Department of Agriculture, providing data and analysis on agriculture, food, the environment, and rural development.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ERS
Target entity description: ERS is the principal economic and social science research agency of the U.S. Department of Agriculture, providing data and analysis on agriculture, food, the environment, and rural development.
  • A. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • B. ER
    ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
  • C. ER
    ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago emergency room.
  • D. REN
    REN is a blockchain-based project and protocol focused on enabling cross-chain liquidity and interoperability between different cryptocurrency networks.
  • E. RE
    RE is the abbreviation for RegioExpress, a category of regional express trains commonly used in European rail transport.
  • 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_69a493d91478819094cc01fb65564bc1 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b82c69c8819082a71e810be34729 completed March 1, 2026, 10:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3bc58d8c8190b9dc7a4bc986abcb completed March 7, 2026, 2:52 p.m.
NEDg Description generation batch_69ac3cf534008190a034d71c90f35efd completed March 7, 2026, 2:57 p.m.
NED2 Entity disambiguation (via description) batch_69ac3d61a0c48190b619b3049df33512 completed March 7, 2026, 2:59 p.m.
Created at: March 1, 2026, 7:41 p.m.