Triple
T24610171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Candlewood Suites |
E609089
|
entity |
| Predicate | kitchenFeature |
P144527
|
FINISHED |
| Object | full-size refrigerator in most suites |
—
|
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: full-size refrigerator in most suites | Statement: [Candlewood Suites, kitchenFeature, full-size refrigerator in most suites]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: kitchenFeature Context triple: [Candlewood Suites, kitchenFeature, full-size refrigerator in most suites]
-
A.
cuisineFeature
Indicates a characteristic, quality, or notable aspect that describes or distinguishes a particular cuisine.
-
B.
featuresOpenKitchen
Indicates that the subject includes or provides an open-style kitchen as part of its layout or design.
-
C.
notableTraitInKitchen
chosen
Indicates that a particular trait, feature, or characteristic is especially prominent or noteworthy within the context of a kitchen.
-
D.
heatingMethod
Indicates the method or technique used to apply heat to something, such as for cooking, warming, or processing.
-
E.
cabinFeatures
Indicates that a cabin possesses or includes specific features, amenities, or characteristics.
- 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_69e2c4d060e08190ac9f7c49b1036e20 |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f2be044d4c819094e14eda28d371a7 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6ca751c8190a040c10d701ecf3a |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:31 a.m.