Triple
T9743464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Triumph of Labour statue |
E236246
|
entity |
| Predicate | hasNumberOfHumanFigures |
P6685
|
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: [Triumph of Labour statue, hasNumberOfHumanFigures, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfHumanFigures Context triple: [Triumph of Labour statue, hasNumberOfHumanFigures, 4]
-
A.
numberOfFiguresDepicted
chosen
Indicates the total count of distinct figures shown within a given depiction or representation.
-
B.
containsHumanFigures
Indicates that the subject includes one or more human figures within its content or composition.
-
C.
hasAnimatedFigures
Indicates that something contains or features figures that are animated or capable of motion.
-
D.
numberOfBronzeFigures
Indicates the quantity of bronze figures associated with a given subject or context.
-
E.
largestFigureLengthApprox
Indicates an approximate measurement of the length of the largest figure involved in the relationship or context.
- 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_69ca84d3e24481908a476e2231123cf9 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f2e5bb081908047e3bf5fe3991c |
completed | April 1, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:23 p.m.