Triple
T505286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | spinning jenny |
E10489
|
entity |
| Predicate | typicalSpindleCount |
P14488
|
FINISHED |
| Object | 8 spindles (early models) |
—
|
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: 8 spindles (early models) | Statement: [spinning jenny, typicalSpindleCount, 8 spindles (early models)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSpindleCount Context triple: [spinning jenny, typicalSpindleCount, 8 spindles (early models)]
-
A.
shaftCount
Indicates the number of shafts associated with or contained in an object or system.
-
B.
hasNumberOfPins
Indicates that an entity is associated with a specific count of pins it possesses or uses.
-
C.
numberOfRivets
Indicates the quantitative relationship specifying how many rivets are associated with a given object or structure.
-
D.
numberOfEngines
Indicates the quantity of engines associated with or used by an entity.
-
E.
valvesPerCylinder
Indicates the number of valves associated with each individual cylinder in an engine.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f14b2acc8190818e8a53eac69c54 |
completed | Feb. 28, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69a2edfce7a08190a408bc019de60d5d |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eebbd70481908b462296671de67b |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.