Triple

T6843149
Position Surface form Disambiguated ID Type / Status
Subject Erna Schneider Hoover E157824 entity
Predicate givenName P17 FINISHED
Object Erna
Erna is the given name of Erna Schneider Hoover, an American mathematician and pioneering computer scientist known for revolutionizing telephone switching systems.
 E623911 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: Erna | Statement: [Erna Schneider Hoover, givenName, Erna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Erna
Context triple: [Erna Schneider Hoover, givenName, Erna]
  • A. Ema
    Ema is a given name used as a variant spelling of Emma in various languages and cultures.
  • B. Maritta
    Maritta is a feminine given name, typically considered a variant of names like Marita or Maria used in various European cultures.
  • C. Magdalena
    Magdalena is the given first name of Swedish opera singer and environmental activist Malena Ernman.
  • D. Eemnes
    Eemnes is a small town and municipality in the central Netherlands known for its characteristic polder landscape and historic village centers.
  • E. Freirina
    Freirina is a small town and commune in northern Chile known for its agricultural activity and historic architecture within the Atacama Region.
  • 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: Erna
Triple: [Erna Schneider Hoover, givenName, Erna]
Generated description
Erna is the given name of Erna Schneider Hoover, an American mathematician and pioneering computer scientist known for revolutionizing telephone switching systems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Erna
Target entity description: Erna is the given name of Erna Schneider Hoover, an American mathematician and pioneering computer scientist known for revolutionizing telephone switching systems.
  • A. Ema
    Ema is a given name used as a variant spelling of Emma in various languages and cultures.
  • B. Maritta
    Maritta is a feminine given name, typically considered a variant of names like Marita or Maria used in various European cultures.
  • C. Magdalena
    Magdalena is the given first name of Swedish opera singer and environmental activist Malena Ernman.
  • D. Eemnes
    Eemnes is a small town and municipality in the central Netherlands known for its characteristic polder landscape and historic village centers.
  • E. Freirina
    Freirina is a small town and commune in northern Chile known for its agricultural activity and historic architecture within the Atacama Region.
  • 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_69c6882ed4c081909dc465a7cf8838be completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d6b7179481909e3482fef47b2719 completed March 27, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69c72fb9a8c08190993807f1a4c54184 completed March 28, 2026, 1:32 a.m.
NEDg Description generation batch_69c735e1d0c48190a3cec055d0eef053 completed March 28, 2026, 1:58 a.m.
NED2 Entity disambiguation (via description) batch_69c7363e2ae481908caceabfb27e3284 completed March 28, 2026, 2 a.m.
Created at: March 27, 2026, 2:19 p.m.