Triple
T12123872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sikyona |
E288758
|
entity |
| Predicate | administrativeCenter |
P1474
|
FINISHED |
| Object | Kiato |
E288757
|
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: Kiato | Statement: [Sikyona, administrativeCenter, Kiato]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiato Context triple: [Sikyona, administrativeCenter, Kiato]
-
A.
Kiato
chosen
Kiato is a coastal town in the northern Peloponnese of Greece, serving as a local commercial and transportation hub within the regional unit of Corinthia.
-
B.
Katito
Katito is a small town in western Kenya that serves as a local commercial and transport center in the former Nyando District of Kisumu County.
-
C.
Kiso
Kiso is a town in Nagano Prefecture, Japan, known for its scenic Kiso Valley, traditional post towns on the old Nakasendō route, and proximity to Mount Ontake.
-
D.
Kitanemuk
Kitanemuk is an extinct Uto-Aztecan language once spoken by the Kitanemuk people in what is now Southern California.
-
E.
Taketa
Taketa is a small historic city in Japan known for its scenic rural landscapes, hot springs, and castle ruins.
- 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_69d6ab4b5e4c81909950b17151eb0951 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91578fd88819099adf55c93d549fc |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e48957081909c4598e82ab31e1f |
completed | May 2, 2026, 3:54 p.m. |
Created at: April 8, 2026, 9:49 p.m.