Triple

T1134872
Position Surface form Disambiguated ID Type / Status
Subject Battle of Zenta E23115 entity
Predicate location P40 FINISHED
Object Senta
Senta is a town in northern Serbia, on the Tisa River, historically notable as the site of the 1697 Battle of Zenta between the Habsburg and Ottoman Empires.
 E138568 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: Senta | Statement: [Battle of Zenta, location, Senta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Senta
Context triple: [Battle of Zenta, location, Senta]
  • A. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • B. Johanna
    Johanna is the given name of Johanna Spyri, the Swiss author best known for creating the classic children's novel "Heidi."
  • C. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • D. Ricarda
    Ricarda is a feminine given name, primarily used in German- and Spanish-speaking countries, derived from the male name Richard.
  • E. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
  • 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: Senta
Triple: [Battle of Zenta, location, Senta]
Generated description
Senta is a town in northern Serbia, on the Tisa River, historically notable as the site of the 1697 Battle of Zenta between the Habsburg and Ottoman Empires.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Senta
Target entity description: Senta is a town in northern Serbia, on the Tisa River, historically notable as the site of the 1697 Battle of Zenta between the Habsburg and Ottoman Empires.
  • A. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • B. Johanna
    Johanna is the given name of Johanna Spyri, the Swiss author best known for creating the classic children's novel "Heidi."
  • C. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • D. Ricarda
    Ricarda is a feminine given name, primarily used in German- and Spanish-speaking countries, derived from the male name Richard.
  • E. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
  • 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_69a493ec75988190b63a11bafaec29b4 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bbfffaa48190b2534ff4da3544ce completed March 1, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac7f30222081909679902c6e0d4790 completed March 7, 2026, 7:40 p.m.
NEDg Description generation batch_69ac7fba00f0819086a0fffa090c5809 completed March 7, 2026, 7:42 p.m.
NED2 Entity disambiguation (via description) batch_69ac807ead9c819088f7195aec87a538 completed March 7, 2026, 7:46 p.m.
Created at: March 1, 2026, 7:44 p.m.