Triple
T23253784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spreckels |
E581808
|
entity |
| Predicate | notableRegionImpact |
P39809
|
FINISHED |
| Object | economic growth in California’s coastal areas |
—
|
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: economic growth in California’s coastal areas | Statement: [Spreckels, notableRegionImpact, economic growth in California’s coastal areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableRegionImpact Context triple: [Spreckels, notableRegionImpact, economic growth in California’s coastal areas]
-
A.
impactRegion
Indicates the geographic or spatial area that is affected or influenced by a particular event, action, or phenomenon.
-
B.
notableImpactCity
Indicates that an entity has had a significant or widely recognized impact on a particular city.
-
C.
notableInRegion
chosen
Indicates that an entity is recognized as notable, prominent, or significant within a specified geographic region.
-
D.
influencesRegion
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
E.
hasRegionalSignificance
Indicates that something holds particular importance, influence, or relevance within a specific geographic region.
- 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_69e24606b17c81908aba1a4911c8a8ba |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f193f9d904819097315c9bf031f667 |
completed | April 29, 2026, 5:15 a.m. |
| PD | Predicate disambiguation | batch_69effce4d704819092826931d430e8c4 |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:11 p.m.