Triple

T6278795
Position Surface form Disambiguated ID Type / Status
Subject Gabourey Sidibe E140727 entity
Predicate familyName P18 FINISHED
Object Sidibe
Sidibe is a surname of West African origin most notably borne by American actress Gabourey Sidibe.
 E582992 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: Sidibe | Statement: [Gabourey Sidibe, familyName, Sidibe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sidibe
Context triple: [Gabourey Sidibe, familyName, Sidibe]
  • A. Sibu
    Sibu is a major town in the central region of Sarawak, Malaysia, known as a commercial and transportation hub on the island of Borneo.
  • B. Ndzebi
    Ndzebi is a Bantu language spoken primarily by the Nzebi people of Gabon and neighboring regions.
  • C. Sanglechi
    Sanglechi is a lesser-known Eastern Iranian language spoken in parts of northeastern Afghanistan and adjacent regions.
  • D. Chambeali
    Chambeali is an Indo-Aryan language spoken primarily in the Chamba region of Himachal Pradesh in northern India.
  • E. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • 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: Sidibe
Triple: [Gabourey Sidibe, familyName, Sidibe]
Generated description
Sidibe is a surname of West African origin most notably borne by American actress Gabourey Sidibe.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sidibe
Target entity description: Sidibe is a surname of West African origin most notably borne by American actress Gabourey Sidibe.
  • A. Sibu
    Sibu is a major town in the central region of Sarawak, Malaysia, known as a commercial and transportation hub on the island of Borneo.
  • B. Ndzebi
    Ndzebi is a Bantu language spoken primarily by the Nzebi people of Gabon and neighboring regions.
  • C. Sanglechi
    Sanglechi is a lesser-known Eastern Iranian language spoken in parts of northeastern Afghanistan and adjacent regions.
  • D. Chambeali
    Chambeali is an Indo-Aryan language spoken primarily in the Chamba region of Himachal Pradesh in northern India.
  • E. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • 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_69c008cc158881908df6ec94a911c736 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c063dc55d48190b5ed48a50f3a742e completed March 22, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c519549cf0819096d01c23f6c915eb completed March 26, 2026, 11:32 a.m.
NEDg Description generation batch_69c51dfe50c4819084c43ca4d6cced35 completed March 26, 2026, 11:52 a.m.
NED2 Entity disambiguation (via description) batch_69c58d97bf808190a2f8f101cf46a16b completed March 26, 2026, 7:48 p.m.
Created at: March 22, 2026, 4:26 p.m.