Triple

T3565275
Position Surface form Disambiguated ID Type / Status
Subject Oscar Neebe E75432 entity
Predicate familyName P18 FINISHED
Object Neebe
Neebe is a surname most notably associated with Oscar Neebe, an American labor activist and one of the defendants in the 1886 Haymarket affair.
 E369375 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: Neebe | Statement: [Oscar Neebe, familyName, Neebe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Neebe
Context triple: [Oscar Neebe, familyName, Neebe]
  • A. Neudeck
    Neudeck is a village in former East Prussia (now Ogrodzieniec in Poland) historically known as the family estate and place of death of German President and World War I field marshal Paul von Hindenburg.
  • B. Finklea
    Finklea is the birth surname of American actress and dancer Cyd Charisse, known for her roles in classic Hollywood musicals.
  • C. Witiges
    Witiges was a 6th-century king of the Ostrogoths best known for leading the Gothic resistance against the Eastern Roman Empire during the Gothic War in Italy.
  • D. Nese
    Nese is an endangered Oceanic language spoken by a small community on the island of Malakula in Vanuatu.
  • E. Bonnell
    Bonnell is the microarchitecture that underpinned Intel's first-generation Atom processors, designed for low-power, energy-efficient computing in mobile and embedded devices.
  • 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: Neebe
Triple: [Oscar Neebe, familyName, Neebe]
Generated description
Neebe is a surname most notably associated with Oscar Neebe, an American labor activist and one of the defendants in the 1886 Haymarket affair.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Neebe
Target entity description: Neebe is a surname most notably associated with Oscar Neebe, an American labor activist and one of the defendants in the 1886 Haymarket affair.
  • A. Neudeck
    Neudeck is a village in former East Prussia (now Ogrodzieniec in Poland) historically known as the family estate and place of death of German President and World War I field marshal Paul von Hindenburg.
  • B. Finklea
    Finklea is the birth surname of American actress and dancer Cyd Charisse, known for her roles in classic Hollywood musicals.
  • C. Witiges
    Witiges was a 6th-century king of the Ostrogoths best known for leading the Gothic resistance against the Eastern Roman Empire during the Gothic War in Italy.
  • D. Nese
    Nese is an endangered Oceanic language spoken by a small community on the island of Malakula in Vanuatu.
  • E. Bonnell
    Bonnell is the microarchitecture that underpinned Intel's first-generation Atom processors, designed for low-power, energy-efficient computing in mobile and embedded devices.
  • 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_69ad85d512708190829c8b2d3a2ccfb8 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc0a8f6288190928479f5bea32245 completed March 8, 2026, 6:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3bbacbb1081908fc57168a8fc3ade completed March 13, 2026, 7:24 a.m.
NEDg Description generation batch_69b3bf78d6a881908d5bcc4ae50a76e5 completed March 13, 2026, 7:40 a.m.
NED2 Entity disambiguation (via description) batch_69b3f5adac0481908b9053585c317be0 completed March 13, 2026, 11:31 a.m.
Created at: March 8, 2026, 3:21 p.m.