Triple

T11133308
Position Surface form Disambiguated ID Type / Status
Subject Unstrut E263342 entity
Predicate hasTributary P415 FINISHED
Object Gera
Gera is a river in Thuringia, Germany, that flows through the city of Erfurt before joining the Unstrut.
 E907873 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: Gera | Statement: [Unstrut, hasTributary, Gera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gera
Context triple: [Unstrut, hasTributary, Gera]
  • A. Gera
    Gera is a city in the German state of Thuringia, known for its industrial heritage and historic architecture along the White Elster river.
  • B. Gera
    Gera is a biblical figure mentioned in the Hebrew Bible, known primarily as a Benjamite ancestor in the genealogy of the tribe of Benjamin.
  • C. Eschwege
    Eschwege is a small historic town in the German state of Hesse, known for its medieval architecture and location near the Werra River.
  • D. Morava
    Morava is a Central European river that forms part of the border between Austria, the Czech Republic, and Slovakia before joining the Danube near Bratislava.
  • E. Lahn
    The Lahn is a river in western Germany that flows through the states of North Rhine-Westphalia, Hesse, and Rhineland-Palatinate before joining the Rhine.
  • 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: Gera
Triple: [Unstrut, hasTributary, Gera]
Generated description
Gera is a river in Thuringia, Germany, that flows through the city of Erfurt before joining the Unstrut.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gera
Target entity description: Gera is a river in Thuringia, Germany, that flows through the city of Erfurt before joining the Unstrut.
  • A. Gera
    Gera is a city in the German state of Thuringia, known for its industrial heritage and historic architecture along the White Elster river.
  • B. Gera
    Gera is a biblical figure mentioned in the Hebrew Bible, known primarily as a Benjamite ancestor in the genealogy of the tribe of Benjamin.
  • C. Eschwege
    Eschwege is a small historic town in the German state of Hesse, known for its medieval architecture and location near the Werra River.
  • D. Morava
    Morava is a Central European river that forms part of the border between Austria, the Czech Republic, and Slovakia before joining the Danube near Bratislava.
  • E. Lahn
    The Lahn is a river in western Germany that flows through the states of North Rhine-Westphalia, Hesse, and Rhineland-Palatinate before joining the Rhine.
  • 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_69d6aa9c0ba08190bbd19c217489b755 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8347a248190837e8c26f25f553a completed April 9, 2026, 5:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69e441f00d7c8190a8fe8e0c1169e6b0 completed April 19, 2026, 2:46 a.m.
NEDg Description generation batch_69e44c09163481909d6a469b1e50b6ff completed April 19, 2026, 3:29 a.m.
NED2 Entity disambiguation (via description) batch_69e4519f4d408190be9863c72006ecad completed April 19, 2026, 3:53 a.m.
Created at: April 8, 2026, 9:28 p.m.