Triple
T15241987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Light Special |
E364278
|
entity |
| Predicate | retailSector |
P72821
|
FINISHED |
| Object | discount department store |
—
|
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: discount department store | Statement: [Blue Light Special, retailSector, discount department store]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: retailSector Context triple: [Blue Light Special, retailSector, discount department store]
-
A.
retailConcept
Indicates that one entity represents a retail-related concept, model, or framework that characterizes or defines the nature of another entity’s retail activity or context.
-
B.
commercialArea
Indicates that the location or region is designated primarily for commercial activities such as businesses, shops, or services.
-
C.
commercialCategory
chosen
Indicates the type of commercial classification or business category under which an entity or transaction is grouped.
-
D.
sector
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
E.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007dcc33081908545ea1a1d2c19fe |
completed | April 15, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69deca899d5c8190be4a7c71e1683c69 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:13 a.m.