Triple
T5959831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palazzo Nicolaci |
E132606
|
entity |
| Predicate | city |
P40
|
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: [Palazzo Nicolaci, city, Noto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Noto Context triple: [Palazzo Nicolaci, city, 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.
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.
-
C.
Enyō
Enyō is a minor Greek goddess associated with war, destruction, and the bloody chaos of battle, often depicted as a companion of Ares.
-
D.
Namba
Namba is a major commercial and entertainment district in Osaka, Japan, known for its bustling nightlife, shopping, and iconic neon-lit streets.
-
E.
Nakanai
Nakanai is an Austronesian language spoken on the island of New Britain in Papua New Guinea, known for its role in the linguistic diversity of the Bismarck Archipelago.
- 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_69c0086c2364819091e9fe2f58fa2517 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c039fbf49881909d97b4abb3c5286d |
completed | March 22, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1083361e08190aaba9e99a856e015 |
completed | March 23, 2026, 9:30 a.m. |
Created at: March 22, 2026, 4:02 p.m.