Triple
T28499326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pompompurin |
E721190
|
entity |
| Predicate | hasThemeCafeLocation |
P168972
|
FINISHED |
| Object | Tokyo |
—
|
NE NERFINISHED |
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: Tokyo | Statement: [Pompompurin, hasThemeCafeLocation, Tokyo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasThemeCafeLocation Context triple: [Pompompurin, hasThemeCafeLocation, Tokyo]
-
A.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
B.
hasLoungeOrCafe
Indicates that one entity provides or includes access to a lounge or café associated with another entity.
-
C.
hasThemedLand
Indicates that one entity (typically a larger venue or park) includes or is composed of a specific themed land or area as part of its layout or structure.
-
D.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
-
E.
hasThemeConnection
Indicates a relationship where one entity is linked to another through a shared or related theme, topic, or conceptual focus.
- F. None of above. chosen
Provenance (4 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_69f01a5afdac8190ac6e72d5c100bd58 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f67866e9248190b7ba218f9ca2ae8d |
completed | May 2, 2026, 10:19 p.m. |
| PD | Predicate disambiguation | batch_69f675fd59608190b246383435e68fce |
completed | May 2, 2026, 10:09 p.m. |
| PDg | Predicate description generation | batch_69f676c35f3481909b9ba18a5662d6ce |
completed | May 2, 2026, 10:12 p.m. |
Created at: April 28, 2026, 3:06 a.m.