Triple
T4830327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | "M’m! M’m! Good!" |
E107928
|
entity |
| Predicate | targetCompanyIndustry |
P59909
|
FINISHED |
| Object | food and beverage |
—
|
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 and beverage | Statement: ["M’m! M’m! Good!", targetCompanyIndustry, food and beverage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetCompanyIndustry Context triple: ["M’m! M’m! Good!", targetCompanyIndustry, food and beverage]
-
A.
targetCompanies
Indicates that certain companies are the intended focus or recipients of a particular action, effort, or objective.
-
B.
industryOfUnderlyingCompany
Indicates the industry sector in which the underlying company associated with this entity operates.
-
C.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
D.
hasParentCompanyIndustry
Indicates that an entity’s parent company operates in, or is associated with, a specified industry.
-
E.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
- F. None of above. chosen
Provenance (4 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_69bd43fac8188190803f0327190621e4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c1fe130819087ae01309f96a0c8 |
completed | March 20, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69bd6dda5e808190a26ec85e4499d8e4 |
completed | March 20, 2026, 3:55 p.m. |
Created at: March 20, 2026, 1:24 p.m.