Triple
T24222686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pure Red Color, Pure Yellow Color, Pure Blue Color |
E601502
|
entity |
| Predicate | numberOfCanvases |
P155249
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Pure Red Color, Pure Yellow Color, Pure Blue Color, numberOfCanvases, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCanvases Context triple: [Pure Red Color, Pure Yellow Color, Pure Blue Color, numberOfCanvases, 3]
-
A.
estimatedNumberOfPaintings
Indicates the approximate count of paintings associated with an entity, rather than an exact, verified number.
-
B.
numberOfCanons
Indicates the quantity of canons associated with or possessed by a given entity.
-
C.
numberOfPaintingsCreated
Indicates the total count of paintings that an entity has created.
-
D.
approximateNumberOfDrawings
Indicates an estimated or rough count of drawings associated with an entity.
-
E.
numberOfPaintedSculptures
Indicates the quantity of sculptures that have been painted in a given context or collection.
- 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_69e29537ca548190b94a37ebe1977caf |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f287dd148081909f02e521ad6043b1 |
completed | April 29, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69f1c448abec8190b87cbf9ed419a309 |
completed | April 29, 2026, 8:41 a.m. |
| PDg | Predicate description generation | batch_69f1c6d4e99081909f61899eccafb73e |
completed | April 29, 2026, 8:52 a.m. |
Created at: April 18, 2026, midnight