Triple
T12451751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UL |
E297548
|
entity |
| Predicate | hasUnderlyingBusinessArea |
P70059
|
FINISHED |
| Object | food products |
—
|
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: food products | Statement: [UL, hasUnderlyingBusinessArea, food products]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUnderlyingBusinessArea Context triple: [UL, hasUnderlyingBusinessArea, food products]
-
A.
hasKeyBusinessArea
Indicates that an entity is associated with or operates within a particular primary business area or domain.
-
B.
isPartOfBusinessArea
Indicates that one entity belongs to, is included within, or falls under the scope of a particular business area.
-
C.
hasUnderlyingCompanyBusinessModel
Indicates that one entity possesses or is based on a specific company business model that underlies its structure, operations, or value creation.
-
D.
hasBusinessTypeAlong
Indicates that a business or commercial entity located along a route, corridor, or area is associated with a specific type or category of business activity.
-
E.
primaryBusinessArea
chosen
Indicates the main field, sector, or domain in which an entity primarily conducts its business activities.
- 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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95151e7348190a1d4953a8b416a13 |
completed | April 10, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69d94d3c27a08190a0237200203e476d |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:56 p.m.