Triple

T11271122
Position Surface form Disambiguated ID Type / Status
Subject Aqib Talib E266813 entity
Predicate givenName P17 FINISHED
Object Aqib
Aqib is a masculine given name of Arabic origin meaning "successor" or "follower."
 E919709 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: Aqib | Statement: [Aqib Talib, givenName, Aqib]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Aqib
Context triple: [Aqib Talib, givenName, Aqib]
  • A. Afridi
    Afridi is a prominent Pashtun tribe of the Karlani confederation, traditionally inhabiting the mountainous regions of present-day Pakistan and Afghanistan.
  • B. Zahir Raheem
    Zahir Raheem is an American former professional boxer best known as a skilled featherweight and lightweight contender who scored notable upsets over several high-profile opponents.
  • C. Hasnat Khan
    Hasnat Khan is a British-Pakistani heart surgeon best known for his romantic relationship with Diana, Princess of Wales.
  • D. Umar Akmal
    Umar Akmal is a Pakistani cricketer known as an aggressive middle-order batsman and occasional wicket-keeper who has represented Pakistan in all three international formats.
  • E. Shaheen Khan
    Shaheen Khan is a British actress best known for her role as the protagonist’s mother in the hit film "Bend It Like Beckham."
  • 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: Aqib
Triple: [Aqib Talib, givenName, Aqib]
Generated description
Aqib is a masculine given name of Arabic origin meaning "successor" or "follower."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Aqib
Target entity description: Aqib is a masculine given name of Arabic origin meaning "successor" or "follower."
  • A. Afridi
    Afridi is a prominent Pashtun tribe of the Karlani confederation, traditionally inhabiting the mountainous regions of present-day Pakistan and Afghanistan.
  • B. Zahir Raheem
    Zahir Raheem is an American former professional boxer best known as a skilled featherweight and lightweight contender who scored notable upsets over several high-profile opponents.
  • C. Hasnat Khan
    Hasnat Khan is a British-Pakistani heart surgeon best known for his romantic relationship with Diana, Princess of Wales.
  • D. Umar Akmal
    Umar Akmal is a Pakistani cricketer known as an aggressive middle-order batsman and occasional wicket-keeper who has represented Pakistan in all three international formats.
  • E. Shaheen Khan
    Shaheen Khan is a British actress best known for her role as the protagonist’s mother in the hit film "Bend It Like Beckham."
  • 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_69d6aac8c2f48190ad0596f1f89f0470 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9506204819089dc0827483bd948 completed April 9, 2026, 6 p.m.
NED1 Entity disambiguation (via context triple) batch_69e542c5fdb88190968831279eaeea49 completed April 19, 2026, 9:01 p.m.
NEDg Description generation batch_69e5474879088190990468d960b26739 completed April 19, 2026, 9:21 p.m.
NED2 Entity disambiguation (via description) batch_69e54eccdd3881908536ee3f9f4ef516 completed April 19, 2026, 9:53 p.m.
Created at: April 8, 2026, 9:31 p.m.