Triple

T3410995
Position Surface form Disambiguated ID Type / Status
Subject MetLife E71892 entity
Predicate tickerSymbol P1447 FINISHED
Object MET
MET is the stock ticker symbol for MetLife, Inc., a major global provider of insurance, annuities, and employee benefit programs.
 E355472 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: MET | Statement: [MetLife, tickerSymbol, MET]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MET
Context triple: [MetLife, tickerSymbol, MET]
  • A. MET
    MET is the standard abbreviation used for the National Hockey League's Metropolitan Division.
  • B. Metro
    Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
  • C. Metro
    Metro is the primary public transportation agency serving Los Angeles County, operating buses, light rail, subway, and other transit services across the region.
  • D. Metro
    Metro is a multinational wholesale and food retail company headquartered in Germany, operating cash-and-carry stores and serving professional customers worldwide.
  • E. Metro
    "Metro" is a Russian disaster thriller film featuring Svetlana Khodchenkova in a prominent role, centered on a catastrophic flood in the Moscow subway system.
  • 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: MET
Triple: [MetLife, tickerSymbol, MET]
Generated description
MET is the stock ticker symbol for MetLife, Inc., a major global provider of insurance, annuities, and employee benefit programs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MET
Target entity description: MET is the stock ticker symbol for MetLife, Inc., a major global provider of insurance, annuities, and employee benefit programs.
  • A. MET
    MET is the standard abbreviation used for the National Hockey League's Metropolitan Division.
  • B. Metro
    Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
  • C. Metro
    "Metro" is a Russian disaster thriller film featuring Svetlana Khodchenkova in a prominent role, centered on a catastrophic flood in the Moscow subway system.
  • D. Metro
    Metro is the primary public transportation agency serving Los Angeles County, operating buses, light rail, subway, and other transit services across the region.
  • E. Metro
    Metro is a multinational wholesale and food retail company headquartered in Germany, operating cash-and-carry stores and serving professional customers worldwide.
  • 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_69ad85ac312481909e7027ced1456a9f completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb9094b2881909262e58a470ed9d0 completed March 8, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69b34bdf81e48190abac8ea645e929ce completed March 12, 2026, 11:27 p.m.
NEDg Description generation batch_69b34e4972008190af3b84f26b4a3629 completed March 12, 2026, 11:37 p.m.
NED2 Entity disambiguation (via description) batch_69b34fc6c3f88190ba1a08243232df05 completed March 12, 2026, 11:44 p.m.
Created at: March 8, 2026, 3:15 p.m.