Triple
T7030184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anatolian beyliks |
E163248
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Karesi beylik |
E587184
|
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: Karesi beylik | Statement: [Anatolian beyliks, hasPart, Karesi beylik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karesi beylik Context triple: [Anatolian beyliks, hasPart, Karesi beylik]
-
A.
Kenderes
Kenderes is a town in Hungary best known as the birthplace and family estate center of Regent Miklós Horthy.
-
B.
Karasi Beylik
chosen
Karasi Beylik was a small 14th-century Turkish principality in northwestern Anatolia that played a key role in the early expansion of the Ottoman Empire across the Dardanelles into the Balkans.
-
C.
Karabulak
Karabulak is a town in the Republic of Ingushetia, Russia, situated in the North Caucasus region.
-
D.
Karaköy
Karaköy is a historic waterfront neighborhood in Istanbul known for its bustling port, cafes, and mix of traditional and modern urban life.
-
E.
Karesz
Karesz is a Hungarian diminutive form of the male given name Károly.
- 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e20dbc8c8190a7446290747d8078 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c775919734819083beb10b4c2b146e |
completed | March 28, 2026, 6:30 a.m. |
Created at: March 27, 2026, 2:35 p.m.