Triple
T4331831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheshire cheese |
E96766
|
entity |
| Predicate | curdTreatment |
P55611
|
FINISHED |
| Object | cut and drained |
—
|
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: cut and drained | Statement: [Cheshire cheese, curdTreatment, cut and drained]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: curdTreatment Context triple: [Cheshire cheese, curdTreatment, cut and drained]
-
A.
treatment
Indicates that one entity is used as a medical or therapeutic intervention to address, manage, or cure a condition affecting another entity.
-
B.
exportTreatment
Indicates the action or process of sending or transferring a treatment (such as a medical, data, or procedural treatment) from one system, location, or context to another for external use or application.
-
C.
treats
Indicates that one entity provides medical care or therapeutic intervention to another entity.
-
D.
importTreatment
Indicates that one entity brings or transfers a treatment or therapeutic intervention into another context, system, or location for use or application.
-
E.
treatmentCountry
Indicates the country where a treatment, therapy, or medical intervention is administered or takes place.
- 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_69b34542fd908190b11b08faad8decfd |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3514dc588819086a4c6d585c1b5b1 |
completed | March 12, 2026, 11:50 p.m. |
| PD | Predicate disambiguation | batch_69b34f4e13fc8190a42c519f37959d27 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b34ff654308190b9717526120d80d3 |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 12, 2026, 11:13 p.m.