Triple
T15215430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Habikino city government |
E363625
|
entity |
| Predicate | governs |
P760
|
FINISHED |
| Object | Habikino |
E73678
|
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: Habikino | Statement: [Habikino city government, governs, Habikino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Habikino Context triple: [Habikino city government, governs, Habikino]
-
A.
Habikino
chosen
Habikino is a city in Osaka Prefecture, Japan, known for its historic kofun burial mounds and role within the Osaka metropolitan area.
-
B.
Hobokan
Hobokan is an alternative name or spelling for the place or entity known as Hobuck.
-
C.
Kakunyo
Kakunyo was a Japanese Buddhist monk of the Jōdo Shinshū tradition, known for systematizing his grandfather Shinran’s teachings and compiling early histories of the sect.
-
D.
Eniwa
Eniwa is a city in Hokkaido, Japan, known for its natural scenery, parks, and proximity to Sapporo.
-
E.
Nakawa
Nakawa is one of the energetic human hosts in Disney’s “Festival of the Lion King” stage show at Disney’s Animal Kingdom.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076e4348819091fa91c1562e7c5c |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef88f8ac881908ca32de44b5aa53a |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:11 a.m.