Triple
T20214660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asif Kapadia |
E493585
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Asif |
—
|
NE NERFINISHED |
How this triple was built (2 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: Asif | Statement: [Asif Kapadia, givenName, Asif]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Asif Context triple: [Asif Kapadia, givenName, Asif]
-
A.
Asif
chosen
Asif is a common male given name used in South Asian and Middle Eastern cultures, notably borne by Pakistani politician Asif Ali Zardari.
-
B.
Bilall
Bilall is a Belgian film director and screenwriter best known as one half of the directing duo Adil & Bilall, recognized for stylish action and crime films like "Bad Boys for Life."
-
C.
Ashfaq
Ashfaq is the given name of Ashfaqulla Khan, an Indian freedom fighter and revolutionary associated with the Hindustan Republican Association during the struggle against British rule.
-
D.
Arif
Arif is a masculine given name commonly used in various cultures, particularly in Arabic-speaking and Muslim-majority countries, meaning "knowledgeable" or "wise."
-
E.
Zafar
Zafar was an important ancient South Arabian city that served as the political and cultural center of the Himyarite Kingdom in what is now Yemen.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ed8101081908e53a8bde48624b1 |
completed | April 20, 2026, 6:22 p.m. |
Created at: April 11, 2026, 11:38 p.m.