Triple

T6066578
Position Surface form Disambiguated ID Type / Status
Subject Nnedi Okorafor E135174 entity
Predicate notableWork P4 FINISHED
Object Noor
Noor is a science fiction novel by Nnedi Okorafor that blends Africanfuturism with themes of identity, technology, and survival in a near-future Nigeria.
 E567263 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: Noor | Statement: [Nnedi Okorafor, notableWork, Noor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Noor
Context triple: [Nnedi Okorafor, notableWork, Noor]
  • A. Noor
    Noor is the American-born widow of King Hussein who served as Queen consort of Jordan and became known for her humanitarian and peace-building work.
  • B. Ranna
    Ranna was a prominent 10th-century Kannada poet, celebrated as one of the “three gems” of early Kannada literature for his influential epic and courtly works.
  • C. Bawi
    Bawi was a Sasanian Persian military commander known for leading forces against the Byzantine Empire during the Iberian War in the 6th century.
  • D. Sihor
    Sihor is a historic town in Gujarat, India, known for its temples, metalwork, and proximity to the city of Bhavnagar.
  • E. Wadee
    Wadee is a given name, likely a variant of the name Wade, used as a personal first name.
  • 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: Noor
Triple: [Nnedi Okorafor, notableWork, Noor]
Generated description
Noor is a science fiction novel by Nnedi Okorafor that blends Africanfuturism with themes of identity, technology, and survival in a near-future Nigeria.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Noor
Target entity description: Noor is a science fiction novel by Nnedi Okorafor that blends Africanfuturism with themes of identity, technology, and survival in a near-future Nigeria.
  • A. Noor
    Noor is the American-born widow of King Hussein who served as Queen consort of Jordan and became known for her humanitarian and peace-building work.
  • B. Ranna
    Ranna was a prominent 10th-century Kannada poet, celebrated as one of the “three gems” of early Kannada literature for his influential epic and courtly works.
  • C. Bawi
    Bawi was a Sasanian Persian military commander known for leading forces against the Byzantine Empire during the Iberian War in the 6th century.
  • D. Sihor
    Sihor is a historic town in Gujarat, India, known for its temples, metalwork, and proximity to the city of Bhavnagar.
  • E. Wadee
    Wadee is a given name, likely a variant of the name Wade, used as a personal first name.
  • 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_69c00878d06881909ee78e88913bf890 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0573f17088190a728f1c290cc9d1d completed March 22, 2026, 8:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69c11d23dca8819080702ca0f05df5dd completed March 23, 2026, 10:59 a.m.
NEDg Description generation batch_69c11dec348c819090715aa1ec407f11 completed March 23, 2026, 11:03 a.m.
NED2 Entity disambiguation (via description) batch_69c11e5efd848190adb834e42b4bdc9e completed March 23, 2026, 11:05 a.m.
Created at: March 22, 2026, 4:10 p.m.