Triple

T3258934
Position Surface form Disambiguated ID Type / Status
Subject Jennifer Lopez E68363 entity
Predicate spouse P13 FINISHED
Object Ojani Noa
Ojani Noa is a Cuban former waiter, model, and actor best known for being Jennifer Lopez’s first husband.
 E341625 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: Ojani Noa | Statement: [Jennifer Lopez, spouse, Ojani Noa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ojani Noa
Context triple: [Jennifer Lopez, spouse, Ojani Noa]
  • A. Yonas
    Yonas is a given name, often used as a variant of Jonas in various cultures.
  • B. Enosh
    Enosh is a biblical figure recognized as a grandson of Adam and an early patriarch in the Book of Genesis.
  • C. Kaan
    Kaan was a powerful Maya royal dynasty associated with the ancient city-state of Calakmul, known for its political and military rivalry with Tikal during the Classic period.
  • D. Jokshan
    Jokshan is a minor biblical figure mentioned in the Book of Genesis as one of the sons of Abraham by his wife Keturah.
  • E. Atoni
    Atoni are an indigenous ethnic group of western Timor known for their traditional hierarchical social structure, distinctive architecture, and dryland farming culture.
  • 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: Ojani Noa
Triple: [Jennifer Lopez, spouse, Ojani Noa]
Generated description
Ojani Noa is a Cuban former waiter, model, and actor best known for being Jennifer Lopez’s first husband.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ojani Noa
Target entity description: Ojani Noa is a Cuban former waiter, model, and actor best known for being Jennifer Lopez’s first husband.
  • A. Yonas
    Yonas is a given name, often used as a variant of Jonas in various cultures.
  • B. Enosh
    Enosh is a biblical figure recognized as a grandson of Adam and an early patriarch in the Book of Genesis.
  • C. Kaan
    Kaan was a powerful Maya royal dynasty associated with the ancient city-state of Calakmul, known for its political and military rivalry with Tikal during the Classic period.
  • D. Jokshan
    Jokshan is a minor biblical figure mentioned in the Book of Genesis as one of the sons of Abraham by his wife Keturah.
  • E. Atoni
    Atoni are an indigenous ethnic group of western Timor known for their traditional hierarchical social structure, distinctive architecture, and dryland farming culture.
  • 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_69ad858f74408190bcbd07f967cd7bd0 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adafa4f40c81909adfd0f7f568e3ce completed March 8, 2026, 5:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b28ed941cc81909c35853e793d6ce5 completed March 12, 2026, 10 a.m.
NEDg Description generation batch_69b2900805d08190afbda5ee5e984b71 completed March 12, 2026, 10:06 a.m.
NED2 Entity disambiguation (via description) batch_69b2ac4c52d48190a87b1e535c2e8a37 completed March 12, 2026, 12:06 p.m.
Created at: March 8, 2026, 3:09 p.m.