Triple

T1923051
Position Surface form Disambiguated ID Type / Status
Subject AlphaZero E40166 entity
Predicate outperforms P6555 FINISHED
Object Elmo
Elmo is a deep contextualized word representation model for natural language processing that captures complex characteristics of word use and syntax across different linguistic contexts.
 E214835 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: Elmo | Statement: [AlphaZero, outperforms, Elmo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Elmo
Context triple: [AlphaZero, outperforms, Elmo]
  • A. Elmo Veron
    Elmo Veron was a film editor known for his work on classic Hollywood productions, including the 1938 drama "Boys Town."
  • B. Kermit
    Kermit is a masculine given name most famously associated with the Muppet frog character created by Jim Henson.
  • C. Grover
    Grover is a masculine given name most famously borne by Grover Cleveland, the 22nd and 24th president of the United States.
  • D. Bert
    Bert is a film director best known for co-directing the 2019 coming-of-age comedy-drama "Troop Zero."
  • E. Bert
    Bert is the given name of Bert Hölldobler, a renowned German behavioral biologist and sociobiologist known for his pioneering research on ants and social insects.
  • 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: Elmo
Triple: [AlphaZero, outperforms, Elmo]
Generated description
Elmo is a deep contextualized word representation model for natural language processing that captures complex characteristics of word use and syntax across different linguistic contexts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Elmo
Target entity description: Elmo is a deep contextualized word representation model for natural language processing that captures complex characteristics of word use and syntax across different linguistic contexts.
  • A. Elmo Veron
    Elmo Veron was a film editor known for his work on classic Hollywood productions, including the 1938 drama "Boys Town."
  • B. Kermit
    Kermit is a masculine given name most famously associated with the Muppet frog character created by Jim Henson.
  • C. Grover
    Grover is a masculine given name most famously borne by Grover Cleveland, the 22nd and 24th president of the United States.
  • D. Bert
    Bert is a film director best known for co-directing the 2019 coming-of-age comedy-drama "Troop Zero."
  • E. Bert
    Bert is the given name of Bert Hölldobler, a renowned German behavioral biologist and sociobiologist known for his pioneering research on ants and social insects.
  • 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_69a8864298748190a2f2fd34f7ef8d77 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb7c51c2881908054760c624dd577 completed March 7, 2026, 5:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3e6678881908d72de7e0f19a648 completed March 8, 2026, 10:10 p.m.
NEDg Description generation batch_69adf471909881909de20d9d1fa0b372 completed March 8, 2026, 10:13 p.m.
NED2 Entity disambiguation (via description) batch_69adf50b17a081909b93ad3e08c71772 completed March 8, 2026, 10:15 p.m.
Created at: March 4, 2026, 7:35 p.m.