Triple
T8783345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matryoshka dolls |
E208985
|
entity |
| Predicate | typicalNumberOfPieces |
P43231
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Matryoshka dolls, typicalNumberOfPieces, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfPieces Context triple: [Matryoshka dolls, typicalNumberOfPieces, 5]
-
A.
numberOfPieces
chosen
Indicates the quantity of discrete parts or units into which something is divided or composed.
-
B.
numberOfChips
Indicates the quantity of chips associated with a given entity or situation.
-
C.
slotCountTypical
Indicates the usual or standard number of slots associated with an entity under normal conditions.
-
D.
typicalPegRange
Indicates the usual or standard range of peg values within which something is normally maintained or expected to operate.
-
E.
typicalDimension
Indicates that one entity represents a standard or characteristic measurement (such as size, length, or capacity) typically associated with 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_69ca836168108190bb43d3dc235c1f55 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f72f4bc8190b85bd051d3db6881 |
completed | March 31, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1aff3881908be6a9cbc9f50461 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:42 p.m.