Triple
T34573763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hakone Open-Air Museum |
E887694
|
entity |
| Predicate | hasApproximateNumberOfOutdoorWorks |
P194327
|
FINISHED |
| Object | over 100 |
—
|
LITERAL 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: over 100 | Statement: [Hakone Open-Air Museum, hasApproximateNumberOfOutdoorWorks, over 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateNumberOfOutdoorWorks Context triple: [Hakone Open-Air Museum, hasApproximateNumberOfOutdoorWorks, over 100]
-
A.
numberOfSculptures
Indicates the quantity of sculptures associated with a given entity or context.
-
B.
estimatedNumberOfPaintings
Indicates the approximate count of paintings associated with an entity, rather than an exact, verified number.
-
C.
hasApproximateNumberOfLocations
Indicates that an entity is associated with an estimated or approximate count of locations, rather than an exact number.
-
D.
numberOfPaintedSculptures
Indicates the quantity of sculptures that have been painted in a given context or collection.
-
E.
hasOutdoorReputation
Indicates that an entity is known or regarded for qualities, activities, or status specifically related to the outdoors.
- 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_69f349d1a5fc81908557a46875b2f157 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fd6a1c1c4881908090053bc359b181 |
completed | May 8, 2026, 4:44 a.m. |
| PD | Predicate disambiguation | batch_69fd696f24d8819091033afacbdaadc5 |
completed | May 8, 2026, 4:41 a.m. |
| PDg | Predicate description generation | batch_69fd6a1a38f081908c573aee4696de4f |
completed | May 8, 2026, 4:44 a.m. |
Created at: May 1, 2026, 2:03 a.m.