Triple
T11390493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Syracuse |
E269822
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Noto |
E127132
|
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: Noto | Statement: [Province of Syracuse, contains, Noto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Noto Context triple: [Province of Syracuse, contains, Noto]
-
A.
Noto
chosen
Noto is a historic town in southeastern Sicily renowned for its exquisite late Baroque architecture and status as a UNESCO World Heritage Site.
-
B.
Nishio
Nishio is a city in Aichi Prefecture, Japan, known for its high-quality matcha green tea production and traditional Japanese culture.
-
C.
Nayot
Nayot is a residential neighborhood in western Jerusalem, Israel, known for its proximity to major cultural and governmental institutions.
-
D.
Teimei
Teimei is the posthumous name of the Japanese empress consort of Emperor Taishō, who served as Empress of Japan in the early 20th century.
-
E.
Nuriro
Nuriro is a class of South Korean intercity passenger trains operated by Korail, providing medium-speed rail services on various routes.
- 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d800160a1c81909d115bf89fe54a49 |
completed | April 9, 2026, 7:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58c8f5ed88190b9cc55c0a73993ec |
completed | April 20, 2026, 2:16 a.m. |
Created at: April 8, 2026, 9:34 p.m.