Triple
T34847659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poker Flat, California |
E1004513
|
entity |
| Predicate | hasNotableFictionalEvent |
P97496
|
FINISHED |
| Object | banishment of outcasts |
—
|
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: banishment of outcasts | Statement: [Poker Flat, California, hasNotableFictionalEvent, banishment of outcasts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFictionalEvent Context triple: [Poker Flat, California, hasNotableFictionalEvent, banishment of outcasts]
-
A.
hasFictionalEventType
Indicates that something is associated with, characterized by, or classified under a particular type or category of fictional event.
-
B.
capturedInFictionalEvent
Indicates that an entity is depicted as being captured during a fictional event or scenario.
-
C.
associatedWithFictionalEvent
chosen
Indicates that an entity has a connection or involvement with a fictional event, such as being based on, inspired by, or participating in that imagined occurrence.
-
D.
hasEventInFiction
Indicates that a fictional work includes or depicts a particular event within its narrative.
-
E.
hasFictionalWar
Indicates that there exists a fictional or imagined war involving the related entities.
- 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_69f76dba76f0819090643cba102c41ec |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a0346b487088190814e4bd0765cb609 |
completed | May 12, 2026, 3:26 p.m. |
| PD | Predicate disambiguation | batch_6a0346428c60819090e5ea815c0921ca |
completed | May 12, 2026, 3:24 p.m. |
Created at: May 3, 2026, 4 p.m.