Triple
T13094157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ahmet Ertegun |
E310536
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ahmet |
E147433
|
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: Ahmet | Statement: [Ahmet Ertegun, givenName, Ahmet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ahmet Context triple: [Ahmet Ertegun, givenName, Ahmet]
-
A.
Ahmet
chosen
Ahmet is a common male given name of Arabic origin, widely used in Turkey and other Muslim-majority countries as a variant of Ahmed.
-
B.
Mehmet
Mehmet is a common Turkish male given name of Arabic origin, widely used across Turkey and among Turkish communities.
-
C.
Fuat
Fuat is a Turkish masculine given name commonly borne by notable figures in politics, academia, and the arts.
-
D.
Mahmut
Mahmut is a masculine given name commonly used in Turkish and related cultures, derived from the Arabic name Mahmoud.
-
E.
Bekir
Bekir is a common Turkish male given name of Arabic origin, often associated with early Islamic history and frequently borne by notable figures in Turkey.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d9813cd1b881909871a318fdd60672 |
completed | April 10, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6f5cd9f2081908c207b21a14233e1 |
completed | May 3, 2026, 7:14 a.m. |
Created at: April 9, 2026, 9:03 p.m.