Triple
T10149318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namba, Osaka, Japan |
E232588
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object | Namba HIPS |
E30526
|
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: Namba HIPS | Statement: [Namba, Osaka, Japan, hasLandmark, Namba HIPS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Namba HIPS Context triple: [Namba, Osaka, Japan, hasLandmark, Namba HIPS]
-
A.
Namba
Namba is a major commercial and entertainment district in Osaka, Japan, known for its bustling nightlife, shopping, and iconic neon-lit streets.
-
B.
Micipsa
Micipsa was a king of Numidia in the 2nd century BCE, known for maintaining an alliance with Rome and overseeing a period of relative stability and prosperity in his kingdom.
-
C.
Namba Hips
chosen
Namba Hips is a distinctive commercial and entertainment complex in Osaka’s Namba district, known for its striking curved façade and shopping, dining, and leisure facilities.
-
D.
Xpu-Ha
Xpu-Ha is a tranquil beach destination on Mexico’s Riviera Maya known for its white sand, turquoise waters, and low-key resorts.
-
E.
HDS
HDS is a high-dispersion spectrograph used on the Subaru Telescope for detailed spectroscopic studies of astronomical objects.
- 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_69ca84885e48819088a31b127cf44904 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec03e80c81909c813dae91c56272 |
completed | April 2, 2026, 4:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e6369c848190984394eedf2f07eb |
completed | April 5, 2026, 10:46 p.m. |
Created at: March 30, 2026, 9:08 p.m.