Triple

T19773552
Position Surface form Disambiguated ID Type / Status
Subject Allergan E474948 entity
Predicate tickerSymbol P1447 FINISHED
Object AGN
AGN was the stock ticker symbol for Allergan, a global pharmaceutical company best known for products such as Botox before its acquisition by AbbVie.
 E245528 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: AGN | Statement: [Allergan, tickerSymbol, AGN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: AGN
Context triple: [Allergan, tickerSymbol, AGN]
  • A. AGN
    AGN is the stock ticker symbol for Aegon, a multinational life insurance, pensions, and asset management company headquartered in the Netherlands.
  • B. AGN
    AGN is the acronym commonly used for the Archivo General de la Nación, the national archive institution responsible for preserving and managing a country's historical and governmental documents.
  • C. AGN
    AGN is a shorthand designation commonly used to refer to the AGN Agreement.
  • D. Blazar
    Blazar is the OpenStack reservation service that enables users to book cloud resources such as compute, storage, and networking in advance for guaranteed availability.
  • E. AGA
    AGA is the Advanced Graphics Architecture chipset used in later Commodore Amiga computers, providing enhanced color depth and graphics capabilities over earlier Amiga chipsets.
  • 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: AGN
Triple: [Allergan, tickerSymbol, AGN]
Generated description
AGN was the stock ticker symbol for Allergan, a global pharmaceutical company best known for products such as Botox before its acquisition by AbbVie.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: AGN
Target entity description: AGN was the stock ticker symbol for Allergan, a global pharmaceutical company best known for products such as Botox before its acquisition by AbbVie.
  • A. AGN chosen
    AGN is the stock ticker symbol for Aegon, a multinational life insurance, pensions, and asset management company headquartered in the Netherlands.
  • B. AGN
    AGN is the acronym commonly used for the Archivo General de la Nación, the national archive institution responsible for preserving and managing a country's historical and governmental documents.
  • C. AGN
    AGN is a shorthand designation commonly used to refer to the AGN Agreement.
  • D. Blazar
    Blazar is the OpenStack reservation service that enables users to book cloud resources such as compute, storage, and networking in advance for guaranteed availability.
  • E. AGA
    AGA is the Advanced Graphics Architecture chipset used in later Commodore Amiga computers, providing enhanced color depth and graphics capabilities over earlier Amiga chipsets.
  • F. None of above.

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_69d8e51a43a08190956bc6df13c91a77 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6535e450c8190a2628245ae0d0bd3 completed April 20, 2026, 4:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07bd76afe08190b958bd49acf21c4a completed May 16, 2026, 12:42 a.m.
NEDg Description generation batch_6a07be5426c48190bb7b0fc315fa2f6a completed May 16, 2026, 12:46 a.m.
NED2 Entity disambiguation (via description) batch_6a07bf9682fc8190a33ea75dba8d88d6 completed May 16, 2026, 12:51 a.m.
Created at: April 10, 2026, 1:48 p.m.