Triple

T10023658
Position Surface form Disambiguated ID Type / Status
Subject Auto-Encoding Variational Bayes E200670 entity
Predicate definesAbbreviation P12874 FINISHED
Object ELBO
ELBO (Evidence Lower Bound) is an objective function used in variational inference to approximate complex probability distributions, particularly in variational autoencoders and related Bayesian models.
 E835245 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: ELBO | Statement: [Auto-Encoding Variational Bayes, definesAbbreviation, ELBO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ELBO
Context triple: [Auto-Encoding Variational Bayes, definesAbbreviation, ELBO]
  • A. ELKB
    ELKB is the Evangelical Lutheran regional church body serving the Protestant community in the German state of Bavaria.
  • B. LLE
    LLE is the station code for Loulé railway station in Portugal’s Algarve region.
  • C. ELP
    ELP is the three-letter IATA airport code for El Paso International Airport, a commercial airport serving El Paso, Texas.
  • D. ELM
    ELM is the commonly used abbreviation for the Estonian Literary Museum, a national research and memory institution dedicated to preserving and studying Estonia’s literary and folkloric heritage.
  • E. ELM
    ELM is the three-letter IATA airport code for Elmira/Corning Regional Airport in New York, United States.
  • 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: ELBO
Triple: [Auto-Encoding Variational Bayes, definesAbbreviation, ELBO]
Generated description
ELBO (Evidence Lower Bound) is an objective function used in variational inference to approximate complex probability distributions, particularly in variational autoencoders and related Bayesian models.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ELBO
Target entity description: ELBO (Evidence Lower Bound) is an objective function used in variational inference to approximate complex probability distributions, particularly in variational autoencoders and related Bayesian models.
  • A. ELKB
    ELKB is the Evangelical Lutheran regional church body serving the Protestant community in the German state of Bavaria.
  • B. LLE
    LLE is the station code for Loulé railway station in Portugal’s Algarve region.
  • C. ELP
    ELP is the three-letter IATA airport code for El Paso International Airport, a commercial airport serving El Paso, Texas.
  • D. ELM
    ELM is the commonly used abbreviation for the Estonian Literary Museum, a national research and memory institution dedicated to preserving and studying Estonia’s literary and folkloric heritage.
  • E. ELM
    ELM is the three-letter IATA airport code for Elmira/Corning Regional Airport in New York, United States.
  • 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_69ca831c45f08190ac1505cc15076608 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd7c75548190aa604d90d63dc111 completed April 2, 2026, 1:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69d26abb0ab08190b5bcf101c5680f3c completed April 5, 2026, 1:59 p.m.
NEDg Description generation batch_69d26cc38274819090cf10c2fcf43cc7 completed April 5, 2026, 2:08 p.m.
NED2 Entity disambiguation (via description) batch_69d26d2c91fc8190bc40a678662c19aa completed April 5, 2026, 2:09 p.m.
Created at: March 30, 2026, 8:53 p.m.