Triple
T7078606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tosk region |
E164886
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Berat |
E422594
|
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: Berat | Statement: [Tosk region, hasCity, Berat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berat Context triple: [Tosk region, hasCity, Berat]
-
A.
Berat
chosen
Berat is a historic city in central Albania renowned for its well-preserved Ottoman architecture and hillside houses, earning it the nickname "the city of a thousand windows" and recognition as a UNESCO World Heritage Site.
-
B.
Waase
Waase is a small village on the island of Ummanz in the German state of Mecklenburg-Vorpommern.
-
C.
Bara
Bara is a town in Pakistan’s Khyber District, known as a key settlement in the Khyber Pass region with strategic and commercial significance.
-
D.
Naju
Naju is a historic city in South Korea known for its pear cultivation and location in the southwestern province of South Jeolla.
-
E.
Tiba
Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
- 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_69c6887cbc6c8190bdfac42d940f4d8a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4ef47d48190b31125d1b57f7bec |
completed | March 27, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7946dbf048190a6307fefeb1dd6a9 |
completed | March 28, 2026, 8:42 a.m. |
Created at: March 27, 2026, 2:40 p.m.