Triple
T21505826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Mars city government |
E530594
|
entity |
| Predicate | promotesIndustry |
P1258
|
FINISHED |
| Object | ice cream industry in Le Mars |
—
|
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: ice cream industry in Le Mars | Statement: [Le Mars city government, promotesIndustry, ice cream industry in Le Mars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: promotesIndustry Context triple: [Le Mars city government, promotesIndustry, ice cream industry in Le Mars]
-
A.
promotes
chosen
Indicates that one entity actively supports, advances, or encourages the growth, adoption, or success of another entity or outcome.
-
B.
supportedIndustry
Indicates that one entity provides backing, resources, or services to help sustain or advance a particular industry.
-
C.
industryActivities
Indicates the types of economic or business activities in which an industry or sector is engaged.
-
D.
sponsorshipIndustry
Indicates a relationship where one entity sponsors another specifically within a given industry or sector context.
-
E.
sponsorIndustry
Indicates that an entity acts as a sponsor for, or is financially or organizationally supporting, a particular industry or industrial sector.
- 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_69e0c45c81f08190a6b8bbb70a45aae7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea7cb5588190a4473bbfe3f49cbb |
completed | April 23, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69e631f6e68081908f5ee4ce7413803e |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:24 p.m.