Triple
T315334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cancer Research UK Cambridge Institute |
E7692
|
entity |
| Predicate | hasLaboratoryType |
P2836
|
FINISHED |
| Object | wet lab |
—
|
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: wet lab | Statement: [Cancer Research UK Cambridge Institute, hasLaboratoryType, wet lab]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLaboratoryType Context triple: [Cancer Research UK Cambridge Institute, hasLaboratoryType, wet lab]
-
A.
hasTypeOfExperiment
Indicates that an experiment is associated with or classified under a specific type or category of experiment.
-
B.
hasFacilityType
chosen
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
C.
hasServiceType
Indicates that an entity is associated with or categorized by a particular type of service.
-
D.
hasCampType
Indicates that an entity is associated with or classified by a particular type or category of camp.
-
E.
hasMaterialType
Indicates that something is composed of, made from, or characterized by a specific type of material.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea6462148190825acc57f6d2adaf |
completed | Feb. 28, 2026, 1:15 p.m. |
| PD | Predicate disambiguation | batch_69a2e9428098819089d5950cd2c96dc4 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.