Triple
T456423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgia Standard Time |
E7241
|
entity |
| Predicate | appliesToCity |
P4810
|
FINISHED |
| Object | Kutaisi |
E7705
|
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: Kutaisi | Statement: [Georgia Standard Time, appliesToCity, Kutaisi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kutaisi Context triple: [Georgia Standard Time, appliesToCity, Kutaisi]
-
A.
Kutaisi
chosen
Kutaisi is one of Georgia’s major cities, historically significant and formerly a capital, located in the western part of the country.
-
B.
Kohat
Kohat is a historic city in northwestern Pakistan known for its strategic location, military cantonment, and role as a regional administrative and commercial center.
-
C.
Valais
Valais is a mountainous canton in southwestern Switzerland known for its Alpine scenery, vineyards, and popular ski resorts such as Zermatt and Verbier.
-
D.
Naha
Naha is the capital and largest city of Okinawa Prefecture in Japan, known as a major political, economic, and cultural center of the Ryukyu Islands.
-
E.
Kamen
Kamen is a surname most prominently associated with American inventor and entrepreneur Dean Kamen, known for creating the Segway and numerous medical devices.
- 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_69a2e7e5c5bc8190a1dc8178218fba40 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2f01ec5148190b74e1727712f1163 |
completed | Feb. 28, 2026, 1:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4ab1982f48190b7d7300f0ab9c637 |
completed | March 1, 2026, 9:09 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.