Triple
T6045370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orhan Gazi |
E134653
|
entity |
| Predicate | honorificTitle |
P2097
|
FINISHED |
| Object | Gazi |
E98410
|
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: Gazi | Statement: [Orhan Gazi, honorificTitle, Gazi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gazi Context triple: [Orhan Gazi, honorificTitle, Gazi]
-
A.
Gazi
chosen
Gazi is an honorific title in Turkey, historically bestowed for distinguished military valor and sacrifice in war.
-
B.
Ziya
Ziya is a masculine given name of Turkish origin, historically associated with notable figures such as sociologist and nationalist thinker Ziya Gökalp.
-
C.
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.
-
D.
Mahmut
Mahmut is a masculine given name commonly used in Turkish and related cultures, derived from the Arabic name Mahmoud.
-
E.
Seyhun
Seyhun is the historical name used in Islamic and Central Asian sources for the Syr Darya River, one of the major rivers of Central Asia.
- 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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056e41f98819089c205ba6138faf0 |
completed | March 22, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1139f53d881908f2aa8211fc7539f |
completed | March 23, 2026, 10:19 a.m. |
Created at: March 22, 2026, 4:09 p.m.