Triple
T12607774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munir Ahmad Khan |
E301028
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Munir |
E160356
|
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: Munir | Statement: [Munir Ahmad Khan, givenName, Munir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Munir Context triple: [Munir Ahmad Khan, givenName, Munir]
-
A.
Munir
chosen
Munir is a masculine given name of Arabic origin meaning "illuminating" or "bright."
-
B.
Nasir
Nasir is a creative work associated with Wyoming Sessions, likely a music release or recording project.
-
C.
Hussain Kirsha
Hussain Kirsha is a character in Naguib Mahfouz’s novel "Midaq Alley," known as the son of a café owner whose ambitions and moral compromises reflect the social changes in mid-20th-century Cairo.
-
D.
Mohammad Nasih
Mohammad Nasih is an Indonesian academic who serves as the rector of Airlangga University, one of the country’s leading public universities.
-
E.
Hassan Ali
Hassan Ali is a given name commonly used in Muslim communities, notably borne by figures such as the 19th-century educationist Hassan Ali Effendi of British India.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954e90efc81909951dbe698afa851 |
completed | April 10, 2026, 7:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ecf3e248190868c1eb864191f8b |
completed | May 2, 2026, 8:30 p.m. |
Created at: April 9, 2026, 5:11 p.m.