Triple
T24220197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Market Blandings |
E601427
|
entity |
| Predicate | hasRoleInStories |
P42552
|
FINISHED |
| Object | local service and market center for Blandings Castle |
—
|
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: local service and market center for Blandings Castle | Statement: [Market Blandings, hasRoleInStories, local service and market center for Blandings Castle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRoleInStories Context triple: [Market Blandings, hasRoleInStories, local service and market center for Blandings Castle]
-
A.
hasStaffTypeInStory
Indicates that a story involves or is associated with a particular type or category of staff.
-
B.
hasManagerInStory
Indicates that one entity serves as the manager of another entity within the context of a specific story or narrative.
-
C.
roleInStories
chosen
Indicates the specific function, position, or character part an entity plays within one or more stories.
-
D.
hasThemeInStory
Indicates that a particular theme is present or plays a significant role within a given story.
-
E.
hasAwardInStory
Indicates that an entity is depicted within a narrative or story as having received a particular award.
- 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_69e29537ca548190b94a37ebe1977caf |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f2820cdd3c8190998d6d901224c09f |
completed | April 29, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69f1c448abec8190b87cbf9ed419a309 |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:59 p.m.