Triple
T12347225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mohammad Nasih |
E294386
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Nasih |
E677345
|
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: Nasih | Statement: [Mohammad Nasih, familyName, Nasih]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nasih Context triple: [Mohammad Nasih, familyName, Nasih]
-
A.
Nasir
Nasir is a creative work associated with Wyoming Sessions, likely a music release or recording project.
-
B.
Naser
chosen
Naser is a masculine given name of Arabic origin, commonly used across the Middle East and Muslim-majority regions, meaning "helper" or "victorious."
-
C.
Nawaf
Nawaf is a masculine given name commonly used in Arabic-speaking countries, often associated with nobility and leadership.
-
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.
Khalaf
Khalaf is an Arabic surname commonly borne by individuals and families across the Middle East and the broader Arab diaspora.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f7ba17481908b03af7316b28d9b |
completed | April 10, 2026, 6:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6346dd09481908f4c80e89e2f4705 |
completed | May 2, 2026, 5:29 p.m. |
Created at: April 8, 2026, 9:53 p.m.