Triple
T22297335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rüstem Pasha |
E551154
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rüstem |
—
|
NE NERFINISHED |
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: Rüstem | Statement: [Rüstem Pasha, givenName, Rüstem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rüstem Context triple: [Rüstem Pasha, givenName, Rüstem]
-
A.
Gaziosmanpaşa
Gaziosmanpaşa is a densely populated residential and commercial district on the European side of Istanbul, known for its rapid urbanization and diverse working- and middle-class communities.
-
B.
Murat
Murat is a historic small town in south-central France, known for its volcanic landscape setting in the Cantal region and its traditional stone architecture.
-
C.
Murad
chosen
Murad is a masculine given name of Arabic origin commonly used in various Muslim-majority cultures.
-
D.
Murad
Murad was an Indian character actor known for his authoritative screen presence in numerous Hindi films from the 1940s through the 1980s.
-
E.
Şahin Bey
Şahin Bey was an Ottoman military officer and national hero known for leading resistance against French forces during the Turkish War of Independence, particularly in the defense of Gaziantep.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e45fb848190a1b2ae21296e3a5f |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15720fba0819080f6c96f6df4f1e0 |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.