Triple
T4621763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kasugayama Primeval Forest |
E101000
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Nara Park |
E100999
|
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: Nara Park | Statement: [Kasugayama Primeval Forest, near, Nara Park]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nara Park Context triple: [Kasugayama Primeval Forest, near, Nara Park]
-
A.
Nara Park
chosen
Nara Park is a famous public park in Nara, Japan, known for its free-roaming deer and historic temples and shrines.
-
B.
Haga Park
Haga Park is a historic royal park in the Stockholm area known for its landscaped grounds, cultural heritage sites, and recreational green spaces.
-
C.
Yongdusan Park
Yongdusan Park is a popular hilltop park in Busan, South Korea, known for its scenic city and harbor views, walking paths, and the iconic Busan Tower.
-
D.
Daewangam Park
Daewangam Park is a coastal park in Ulsan, South Korea, known for its dramatic seaside cliffs, pine forest trails, and views of the Daewangam Rock formation.
-
E.
Taejongdae Park
Taejongdae Park is a scenic coastal park in Busan, South Korea, famous for its dramatic seaside cliffs, lighthouse views, and walking trails overlooking the ocean.
- 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_69bd43d0497c8190ac23c65c5804846a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a0374308190aeaffc9d866a3742 |
completed | March 20, 2026, 2:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfa9b38f08190b7d96d9d72495cb9 |
completed | March 21, 2026, 1:55 a.m. |
Created at: March 20, 2026, 1:12 p.m.