Triple
T10537793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Courbevoie |
E248615
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Kudamatsu |
E1021311
|
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: Kudamatsu | Statement: [Courbevoie, hasTwinTown, Kudamatsu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kudamatsu Context triple: [Courbevoie, hasTwinTown, Kudamatsu]
-
A.
Kudamatsu
chosen
Kudamatsu is a coastal city in western Japan known for its industrial facilities and location along the Seto Inland Sea in Yamaguchi Prefecture.
-
B.
Kanramachi
Kanramachi is a Japanese town known for its cultural and municipal partnership with the Italian town of Certaldo.
-
C.
Kumagaya
Kumagaya is a city in northern Saitama Prefecture, Japan, known for its hot summer temperatures and role as a regional commercial and transportation hub.
-
D.
Shibukawa
Shibukawa is a city in Gunma Prefecture, Japan, known as a regional transport hub and gateway to nearby hot spring resorts such as Ikaho Onsen.
-
E.
Fukusaki
Fukusaki is a town in Hyōgo Prefecture, Japan, known for its rural setting and association with folklorist Kunio Yanagita.
- 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50a56133c819088285522e64831f7 |
completed | April 7, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a82a0cf48190a201a533c7387512 |
completed | May 3, 2026, 7:55 p.m. |
Created at: April 6, 2026, 12:31 p.m.