Triple
T3314409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zafar |
E69646
|
entity |
| Predicate | penName |
P3799
|
FINISHED |
| Object | Zafar |
E69646
|
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: Zafar | Statement: [Zafar, penName, Zafar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zafar Context triple: [Zafar, penName, Zafar]
-
A.
Zafar
chosen
Zafar was the pen name of Bahadur Shah II, the last Mughal emperor of India and a noted Urdu poet.
-
B.
Thadiq
Thadiq is a town in central Saudi Arabia known for its traditional architecture and location within the Riyadh administrative region.
-
C.
Ilyas
Ilyas is the Arabic and Quranic form of the prophet Elijah, revered in Islamic tradition as a righteous messenger of God.
-
D.
Fayyazuddin
Fayyazuddin is a Pakistani theoretical physicist known for his contributions to particle physics and for being a prominent student and collaborator of Nobel laureate Abdus Salam.
-
E.
Razzak
Razzak was a legendary Bangladeshi film actor, often hailed as "Nayak Raj," who became one of the most iconic and influential stars in Bengali cinema.
- 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_69ad85a0bb048190a5458d2738012d61 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb10f97b48190afb9c3864faf8cb2 |
completed | March 8, 2026, 5:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3342118a48190b6d53fbf1b6c2a7d |
completed | March 12, 2026, 9:46 p.m. |
Created at: March 8, 2026, 3:11 p.m.