Triple
T5181281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | River Rivelin |
E116925
|
entity |
| Predicate | numberOfRecordedMillsAndWheels |
P62218
|
FINISHED |
| Object | around 20 to 21 |
—
|
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: around 20 to 21 | Statement: [River Rivelin, numberOfRecordedMillsAndWheels, around 20 to 21]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRecordedMillsAndWheels Context triple: [River Rivelin, numberOfRecordedMillsAndWheels, around 20 to 21]
-
A.
numberOfSpokes
Indicates the count of individual spokes associated with or contained in a given object or structure.
-
B.
hasNumberOfSpokes
Indicates the relationship that specifies how many spokes are present in or associated with an object.
-
C.
numberOfWheels
Indicates the quantity of wheels that an entity possesses or is associated with.
-
D.
shaftCount
Indicates the number of shafts associated with or contained in an object or system.
-
E.
typicalSpindleCount
Indicates the usual or standard number of spindles associated with an entity in this relationship.
- 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_69bd446140f08190becb93c61158f27f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd799bc58c819098a8e91e21baaef4 |
completed | March 20, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69bd77b529948190b86671ebe43f4734 |
completed | March 20, 2026, 4:37 p.m. |
| PDg | Predicate description generation | batch_69bd79251b548190918a1eb930e24c22 |
completed | March 20, 2026, 4:43 p.m. |
Created at: March 20, 2026, 1:45 p.m.