Triple
T4830397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sunflowers (Paris series) |
E107929
|
entity |
| Predicate | numberOfWorksApproximate |
P6221
|
FINISHED |
| Object | several |
—
|
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: several | Statement: [Sunflowers (Paris series), numberOfWorksApproximate, several]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWorksApproximate Context triple: [Sunflowers (Paris series), numberOfWorksApproximate, several]
-
A.
numberOfWorks
chosen
Indicates the total count of works associated with a given entity.
-
B.
numberOfWorksCreated
Indicates the total count of creative works that an entity has produced or authored.
-
C.
numberOfManuscriptsApprox
Indicates an approximate count of manuscripts associated with an entity.
-
D.
estimatedVolumeOfWritings
Indicates the approximate total quantity or volume of writings attributed to or produced by an entity.
-
E.
worksCollectedBy
Indicates that one entity gathers, compiles, or curates the works (such as creations, publications, or outputs) produced by another entity.
- 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_69bd43fac8188190803f0327190621e4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c1fe130819087ae01309f96a0c8 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:24 p.m.