Triple
T4137546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tevfik Fikret |
E89191
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mehmet |
E230556
|
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: Mehmet | Statement: [Tevfik Fikret, givenName, Mehmet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mehmet Context triple: [Tevfik Fikret, givenName, Mehmet]
-
A.
Mehmet
chosen
Mehmet is a common Turkish male given name of Arabic origin, widely used across Turkey and among Turkish communities.
-
B.
Ahmet
Ahmet is a common male given name of Arabic origin, widely used in Turkey and other Muslim-majority countries as a variant of Ahmed.
-
C.
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.
-
D.
Mustafa
Mustafa is the given birth name of Mustafa Kemal Atatürk, the founder and first president of the Republic of Turkey.
-
E.
Selim Işık
Selim Işık is a central, tragicomic character in Oğuz Atay’s novel "Tutunamayanlar," symbolizing the alienated intellectual who cannot adapt to modern Turkish society.
- 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_69aed95785788190ae75bcf0cd1cafdf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af02345c2c819090a9db6b375a7fc7 |
completed | March 9, 2026, 5:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589e13e5881909c52e04875afc542 |
completed | March 14, 2026, 4:16 p.m. |
Created at: March 9, 2026, 3:43 p.m.