Triple
T242131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HauteLook |
E4953
|
entity |
| Predicate | discountType |
P7916
|
FINISHED |
| Object | off-price retail |
—
|
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: off-price retail | Statement: [HauteLook, discountType, off-price retail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: discountType Context triple: [HauteLook, discountType, off-price retail]
-
A.
fareDiscount
Indicates that a reduced price is applied to a standard fare for a product or service.
-
B.
typeOfIncentive
chosen
Indicates the specific kind or category of incentive associated with an entity or action.
-
C.
offersProgram
Indicates that an entity provides or makes available a specific program (such as a course, curriculum, or initiative).
-
D.
customerType
Indicates the classification or category assigned to a customer based on their characteristics, status, or relationship with a business.
-
E.
offersDiscipline
Indicates that one entity provides or makes available a particular field of study, training, or area of specialization to another entity.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d35aa288190966b6e15af1525cb |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b60ad308190b12f119960a8bde7 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.