Triple
T13720299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Odawara |
E329012
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Nakai |
E395010
|
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: Nakai | Statement: [Odawara, borderedBy, Nakai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nakai Context triple: [Odawara, borderedBy, Nakai]
-
A.
Nakai
chosen
Nakai is a small town in Kanagawa Prefecture, Japan, known for its rural character and proximity to larger cities like Hadano.
-
B.
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.
-
C.
Nakawa
Nakawa is one of the energetic human hosts in Disney’s “Festival of the Lion King” stage show at Disney’s Animal Kingdom.
-
D.
Nagayama
Nagayama is a residential and commercial district within Tama New Town in Tokyo, Japan, known for its planned urban layout and local amenities.
-
E.
Nakata
Nakata is a Japanese surname most famously associated with former professional footballer Hidetoshi Nakata, one of Japan’s best-known international players.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd439a121c81908cae964e7756274c |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd192957008190b525778430b56ca0 |
completed | May 7, 2026, 10:58 p.m. |
Created at: April 9, 2026, 9:55 p.m.