Triple
T6653134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whitney |
E150871
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Kashif |
E240592
|
NE FINISHED |
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: Kashif | Statement: [Whitney, producer, Kashif]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kashif Context triple: [Whitney, producer, Kashif]
-
A.
Kashif
chosen
Kashif was an American R&B singer, songwriter, and producer known for his influential 1980s work that helped shape the post-disco and urban contemporary sound.
-
B.
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.
-
C.
Zafar
Zafar was the pen name of Bahadur Shah II, the last Mughal emperor of India and a noted Urdu poet.
-
D.
Majeed Amjad
Majeed Amjad was a prominent 20th-century Urdu poet known for his introspective, modernist verse and significant contribution to Pakistani literature.
-
E.
Tariq Anwar
Tariq Anwar is a British film editor known for his acclaimed work on numerous major films, including the Academy Award–winning drama "The King’s Speech."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c687f2c9508190a60b9aad31d3f358 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b046c1408190af288575e40936d0 |
completed | March 27, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eefd800c8190806cf3dff204ca01 |
completed | March 27, 2026, 8:56 p.m. |
Created at: March 27, 2026, 2:01 p.m.