Triple

T363231
Position Surface form Disambiguated ID Type / Status
Subject Tektronix E7900 entity
Predicate formerName P65 FINISHED
Object Tekrad
Tekrad was the original name of Tektronix, an American company known for its pioneering electronic test and measurement equipment.
E46081 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: Tekrad | Statement: [Tektronix, formerName, Tekrad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tekrad
Context triple: [Tektronix, formerName, Tekrad]
  • A. Kraftt
    Kraftt is an alternative or variant form of the name "Kraft," likely referring to the same or a closely related entity, such as the well-known food brand or surname.
  • B. Ta-Mehu
    Ta-Mehu is the ancient Egyptian name for Lower Egypt, the northern region of the Nile Valley encompassing the Nile Delta.
  • C. Teda
    Teda are a Saharan ethnic group, primarily inhabiting northern Chad and surrounding regions, known for their nomadic lifestyle and Tebu language.
  • D. Tverya
    Tverya is the Hebrew name for Tiberias, an ancient city in northern Israel on the western shore of the Sea of Galilee known for its religious significance and hot springs.
  • E. Klecko
    Klecko is the surname of former American football defensive lineman Joe Klecko, best known for his standout career with the New York Jets as part of the “New York Sack Exchange.”
  • 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: Tekrad
Triple: [Tektronix, formerName, Tekrad]
Generated description
Tekrad was the original name of Tektronix, an American company known for its pioneering electronic test and measurement equipment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tekrad
Target entity description: Tekrad was the original name of Tektronix, an American company known for its pioneering electronic test and measurement equipment.
  • A. Kraftt
    Kraftt is an alternative or variant form of the name "Kraft," likely referring to the same or a closely related entity, such as the well-known food brand or surname.
  • B. Ta-Mehu
    Ta-Mehu is the ancient Egyptian name for Lower Egypt, the northern region of the Nile Valley encompassing the Nile Delta.
  • C. Teda
    Teda are a Saharan ethnic group, primarily inhabiting northern Chad and surrounding regions, known for their nomadic lifestyle and Tebu language.
  • D. Tverya
    Tverya is the Hebrew name for Tiberias, an ancient city in northern Israel on the western shore of the Sea of Galilee known for its religious significance and hot springs.
  • E. Klecko
    Klecko is the surname of former American football defensive lineman Joe Klecko, best known for his standout career with the New York Jets as part of the “New York Sack Exchange.”
  • 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_69a2e7e880008190a6ad7e06e5d03007 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ebd1016481909b8ba3b047a47145 completed Feb. 28, 2026, 1:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3e86533a481909bab5f0b52114c6a completed March 1, 2026, 7:19 a.m.
NEDg Description generation batch_69a3e99fd210819099e83cd183a4daa7 completed March 1, 2026, 7:24 a.m.
NED2 Entity disambiguation (via description) batch_69a3ea45c86c8190a6c215430601ad15 completed March 1, 2026, 7:27 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.