Triple
T2714236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sal Khan |
E59930
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Salman |
E29982
|
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: Salman | Statement: [Sal Khan, givenName, Salman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salman Context triple: [Sal Khan, givenName, Salman]
-
A.
Salman
chosen
Salman is the given name of Salman Rushdie, the renowned British-Indian novelist known for works such as "Midnight's Children" and "The Satanic Verses."
-
B.
Salman Amin Khan
Salman Amin Khan is an American educator and entrepreneur best known as the founder of Khan Academy, a nonprofit organization providing free online educational resources worldwide.
-
C.
Salman Khan
Salman Khan is an American educator and entrepreneur best known as the founder of the online learning platform Khan Academy.
-
D.
Shahrukh Mirza
Shahrukh Mirza was a 15th-century Timurid ruler who consolidated and governed much of Iran and Central Asia, fostering a flourishing of Persian culture, arts, and architecture.
-
E.
Kamal
Kamal is a given name shared by various notable individuals across politics, arts, and other fields in the Arabic-speaking world and beyond.
- 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_69ab4ac92a088190bc74bca14038e3de |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda93a2388190b55a1c821ef6bc64 |
completed | March 7, 2026, 7:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbbe621881909e153290394798d6 |
completed | March 10, 2026, 6:35 a.m. |
Created at: March 6, 2026, 9:55 p.m.