Triple
T17786975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yamaguchi Prefecture |
E444041
|
entity |
| Predicate | hasHistoricCity |
P3786
|
FINISHED |
| Object | Hagi |
—
|
NE NERFINISHED |
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: Hagi | Statement: [Yamaguchi Prefecture, hasHistoricCity, Hagi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hagi Context triple: [Yamaguchi Prefecture, hasHistoricCity, Hagi]
-
A.
Hagi
chosen
Hagi is a historic castle town in Yamaguchi Prefecture, Japan, known for its well-preserved samurai districts, traditional streets, and role in the late Edo and Meiji Restoration periods.
-
B.
Hagi
Hagi is a Romanian surname most famously associated with Gheorghe Hagi, one of Romania’s greatest footballers.
-
C.
Miyoshi
Miyoshi is a Japanese city known for its scenic river valleys, historical sites, and cultural exchanges with its international sister cities.
-
D.
Hadano
Hadano is a city in Kanagawa Prefecture, Japan, known for its natural scenery, hiking trails, and proximity to the Tanzawa Mountains.
-
E.
Harada
Harada is a fictional character from the X-Men film universe, depicted as a skilled Japanese warrior and bodyguard in "The Wolverine."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9ef17708190bdf7e2adbf14ddc2 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e487938418819096016ad717b014e6 |
completed | April 19, 2026, 7:43 a.m. |
Created at: April 10, 2026, 10:12 a.m.