Triple

T8405495
Position Surface form Disambiguated ID Type / Status
Subject Joseph Harroz Jr. E198486 entity
Predicate familyName P18 FINISHED
Object Harroz
Harroz is the surname of Joseph Harroz Jr., an American academic administrator and president of the University of Oklahoma.
 E732603 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: Harroz | Statement: [Joseph Harroz Jr., familyName, Harroz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Harroz
Context triple: [Joseph Harroz Jr., familyName, Harroz]
  • A. Tarro
    Tarro is a suburban railway station in the Hunter Region of New South Wales, Australia, serving the local community on the Main Northern line.
  • B. Segeda
    Segeda was a prominent ancient Celtiberian city in what is now northeastern Spain, known for its role in the Celtiberian Wars against Rome.
  • C. Farino
    Farino is a small rural commune in the South Province of New Caledonia, known for its lush forests and eco-tourism activities.
  • D. Canillejas
    Canillejas is a Madrid Metro station serving the Canillejas neighborhood in the San Blas-Canillejas district of Madrid, Spain.
  • E. Menua
    Menua was a prominent king of the ancient kingdom of Urartu, known for expanding its territory and developing extensive irrigation and fortification projects.
  • 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: Harroz
Triple: [Joseph Harroz Jr., familyName, Harroz]
Generated description
Harroz is the surname of Joseph Harroz Jr., an American academic administrator and president of the University of Oklahoma.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Harroz
Target entity description: Harroz is the surname of Joseph Harroz Jr., an American academic administrator and president of the University of Oklahoma.
  • A. Tarro
    Tarro is a suburban railway station in the Hunter Region of New South Wales, Australia, serving the local community on the Main Northern line.
  • B. Segeda
    Segeda was a prominent ancient Celtiberian city in what is now northeastern Spain, known for its role in the Celtiberian Wars against Rome.
  • C. Farino
    Farino is a small rural commune in the South Province of New Caledonia, known for its lush forests and eco-tourism activities.
  • D. Canillejas
    Canillejas is a Madrid Metro station serving the Canillejas neighborhood in the San Blas-Canillejas district of Madrid, Spain.
  • E. Menua
    Menua was a prominent king of the ancient kingdom of Urartu, known for expanding its territory and developing extensive irrigation and fortification projects.
  • 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_69ca8310df9c8190b25f16161cca3e41 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cb8312941c8190af0b2def0a4e02be completed March 31, 2026, 8:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce02f8596c8190a61b6f1ffd5a609c completed April 2, 2026, 5:47 a.m.
NEDg Description generation batch_69ce077f25648190b9a95fb72f5b4f8c completed April 2, 2026, 6:06 a.m.
NED2 Entity disambiguation (via description) batch_69ce08e192088190ad8170b1bedd568d completed April 2, 2026, 6:12 a.m.
Created at: March 30, 2026, 6:05 p.m.