Triple
T6177265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TK Maxx |
E137850
|
entity |
| Predicate | typicalDiscountRange |
P13474
|
FINISHED |
| Object | up to 60 percent off recommended retail prices |
—
|
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: up to 60 percent off recommended retail prices | Statement: [TK Maxx, typicalDiscountRange, up to 60 percent off recommended retail prices]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalDiscountRange Context triple: [TK Maxx, typicalDiscountRange, up to 60 percent off recommended retail prices]
-
A.
discountRate
Indicates the percentage or amount by which a price, cost, or value is reduced relative to its original level.
-
B.
fareDiscount
Indicates that a reduced price is applied to a standard fare for a product or service.
-
C.
offersDiscountsOn
Indicates that one entity provides price reductions or special discount deals specifically applied to another entity or its associated items or services.
-
D.
typicalRange
chosen
Indicates the usual or expected range of values, conditions, or states within which something normally occurs or applies.
-
E.
salePriceApproximate
Indicates that the recorded sale price is an estimated or approximate value rather than an exact amount.
- 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_69c008a80f748190ba3d07ffc81acb29 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05dc87bc48190834042d9c41d5b86 |
completed | March 22, 2026, 9:23 p.m. |
| PD | Predicate disambiguation | batch_69c055f7f12881908e21c04e9b752ba4 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:18 p.m.