Triple
T17711064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gazis |
E441566
|
entity |
| Predicate | pluralFormOf |
P5088
|
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: [Gazis, pluralFormOf, Gazi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gazi Context triple: [Gazis, pluralFormOf, Gazi]
-
A.
Gazi
chosen
Gazi is an honorific title in Turkey, historically bestowed for distinguished military valor and sacrifice in war.
-
B.
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.
-
C.
Melikgazi
Melikgazi is a central district and municipality of the city of Kayseri in central Turkey, known as one of the province’s main urban and administrative hubs.
-
D.
Kadir
Kadir is a masculine given name of Turkish origin commonly used in Turkey and among Turkish-speaking communities.
-
E.
Ziya
Ziya is a masculine given name of Turkish origin, historically associated with notable figures such as sociologist and nationalist thinker Ziya Gökalp.
- 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_69d8b9ea20b48190ace88bb46b01e6a9 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4729b5d3c819085613ed25dc6761d |
completed | April 19, 2026, 6:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a023025ae808190ba3e9a875bfc68e9 |
completed | May 11, 2026, 7:38 p.m. |
Created at: April 10, 2026, 10:05 a.m.