Triple
T34573764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hakone Open-Air Museum |
E887694
|
entity |
| Predicate | hasApproximateNumberOfPicassoWorks |
P5764
|
FINISHED |
| Object | over 300 |
—
|
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 300 | Statement: [Hakone Open-Air Museum, hasApproximateNumberOfPicassoWorks, over 300]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateNumberOfPicassoWorks Context triple: [Hakone Open-Air Museum, hasApproximateNumberOfPicassoWorks, over 300]
-
A.
estimatedNumberOfPaintings
chosen
Indicates the approximate count of paintings associated with an entity, rather than an exact, verified number.
-
B.
timeInPicassoLife
Indicates the specific period or phase within Picasso’s life during which an associated event, work, or relationship occurred.
-
C.
hasFamousArtwork
Indicates that an entity possesses or is associated with a well-known or widely recognized artwork.
-
D.
numberOfPaintingsCreated
Indicates the total count of paintings that an entity has created.
-
E.
numberOfBlackPaintings
Indicates the count of paintings in the subject entity that are classified as black paintings.
- F. None of above.
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_69f349d1a5fc81908557a46875b2f157 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fd6dbd1b648190b1a0b391c03aebc5 |
completed | May 8, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69fd6a9020548190bbfa845360ac85fb |
completed | May 8, 2026, 4:46 a.m. |
Created at: May 1, 2026, 2:03 a.m.