Triple
T242183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anniversary Sale |
E4954
|
entity |
| Predicate | businessImpact |
P2313
|
FINISHED |
| Object | key revenue driver for Nordstrom |
—
|
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: key revenue driver for Nordstrom | Statement: [Anniversary Sale, businessImpact, key revenue driver for Nordstrom]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: businessImpact Context triple: [Anniversary Sale, businessImpact, key revenue driver for Nordstrom]
-
A.
socialImpact
Indicates the extent to which an action, entity, or relationship affects society or communities, whether positively or negatively.
-
B.
economicAspect
chosen
Indicates that something is related to, characterized by, or has implications for economic factors, conditions, or outcomes.
-
C.
businessVenture
Indicates a relationship where entities jointly engage in, operate, or are involved with a commercial or entrepreneurial undertaking.
-
D.
economicDamage
Indicates that one entity causes or experiences financial loss, harm, or negative economic impact as a result of another entity or event.
-
E.
economicTrend
Indicates the general direction or pattern of economic activity or conditions over a period of time.
- 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.