Triple
T16141525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel Greg |
E391666
|
entity |
| Predicate | numberOfMillsOwned |
P62218
|
FINISHED |
| Object | multiple cotton mills in northwest England |
—
|
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: multiple cotton mills in northwest England | Statement: [Samuel Greg, numberOfMillsOwned, multiple cotton mills in northwest England]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMillsOwned Context triple: [Samuel Greg, numberOfMillsOwned, multiple cotton mills in northwest England]
-
A.
hasNumberOfHouses
Indicates the quantity of houses associated with a given entity.
-
B.
numberOfRecordedMillsAndWheels
chosen
Indicates the total count of mills and wheels that have been recorded or documented for a given subject.
-
C.
numberOfMansions
Indicates the quantity of mansions associated with a given entity.
-
D.
numberOfChips
Indicates the quantity of chips associated with a given entity or situation.
-
E.
milesCanBePurchased
Indicates that a certain number of miles are eligible to be bought, typically in exchange for money or points.
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21a087c848190aba9ed2ccb422427 |
completed | April 17, 2026, 11:31 a.m. |
| PD | Predicate disambiguation | batch_69e182885bc08190822ae7e8a4b8ac1f |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 5:01 a.m.