Triple

T409154
Position Surface form Disambiguated ID Type / Status
Subject APEC Ministerial Meeting E9448 entity
Predicate shortName P43 FINISHED
Object AMM
AMM is the commonly used abbreviation for the APEC Ministerial Meeting, the annual gathering of Asia-Pacific Economic Cooperation foreign and trade ministers.
 E51922 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: AMM | Statement: [APEC Ministerial Meeting, shortName, AMM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: AMM
Context triple: [APEC Ministerial Meeting, shortName, AMM]
  • A. AMRO
    AMRO is the World Health Organization’s Regional Office responsible for public health leadership and coordination across the Americas.
  • B. AMTK
    AMTK is the reporting mark used by Amtrak, the United States’ national passenger railroad service, to identify its locomotives and rolling stock.
  • C. AMX
    AMX is a Dutch stock market index that tracks the performance of mid-cap companies listed on Euronext Amsterdam.
  • D. AMLA 2020
    AMLA 2020 is a U.S. federal law that significantly updated and expanded the country’s anti–money laundering and counter-terrorist financing framework, including reforms to beneficial ownership reporting and enforcement powers.
  • E. AICUM
    AICUM is an academic or research-related organization associated with George Washington University.
  • 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: AMM
Triple: [APEC Ministerial Meeting, shortName, AMM]
Generated description
AMM is the commonly used abbreviation for the APEC Ministerial Meeting, the annual gathering of Asia-Pacific Economic Cooperation foreign and trade ministers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: AMM
Target entity description: AMM is the commonly used abbreviation for the APEC Ministerial Meeting, the annual gathering of Asia-Pacific Economic Cooperation foreign and trade ministers.
  • A. AMRO
    AMRO is the World Health Organization’s Regional Office responsible for public health leadership and coordination across the Americas.
  • B. AMTK
    AMTK is the reporting mark used by Amtrak, the United States’ national passenger railroad service, to identify its locomotives and rolling stock.
  • C. AMX
    AMX is a Dutch stock market index that tracks the performance of mid-cap companies listed on Euronext Amsterdam.
  • D. AMLA 2020
    AMLA 2020 is a U.S. federal law that significantly updated and expanded the country’s anti–money laundering and counter-terrorist financing framework, including reforms to beneficial ownership reporting and enforcement powers.
  • E. AICUM
    AICUM is an academic or research-related organization associated with George Washington University.
  • 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_69a2e80111fc8190961d5b7c6154123f completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2ecc098c4819088d127c5ea55ced9 completed Feb. 28, 2026, 1:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69a41777d794819099a07555ad2defe2 completed March 1, 2026, 10:39 a.m.
NEDg Description generation batch_69a418da55088190935babe9abae5ac4 completed March 1, 2026, 10:45 a.m.
NED2 Entity disambiguation (via description) batch_69a4196334948190a1f6004b1a292550 completed March 1, 2026, 10:48 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.