Triple
T26401059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lviny Most |
E663697
|
entity |
| Predicate | hasNumberOfLionSculptures |
P22479
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Lviny Most, hasNumberOfLionSculptures, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfLionSculptures Context triple: [Lviny Most, hasNumberOfLionSculptures, 4]
-
A.
numberOfSculptures
chosen
Indicates the quantity of sculptures associated with a given entity or context.
-
B.
lionNumber
Indicates a relationship where a specific number is associated with or assigned to a lion (or lions), such as a count, identifier, or quantity.
-
C.
lionStatuesModeledAfter
Indicates that certain lion statues are created or designed to resemble or be based on specific original models or references.
-
D.
hasSculpturalFigures
Indicates that something includes or features three-dimensional sculpted figures as part of its form or decoration.
-
E.
numberOfPaintedSculptures
Indicates the quantity of sculptures that have been painted in a given context or collection.
- 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_69ee883823988190b418b111be28a44a |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f707f7959881908f037f0d6b1d0c36 |
completed | May 3, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69f700fc274c8190a128593dc7c7abd0 |
completed | May 3, 2026, 8:02 a.m. |
Created at: April 26, 2026, 11:32 p.m.