Triple
T19454035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hawkins General Hospital |
E486689
|
entity |
| Predicate | servesFictionalPopulation |
P49690
|
FINISHED |
| Object | residents of Hawkins |
—
|
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: residents of Hawkins | Statement: [Hawkins General Hospital, servesFictionalPopulation, residents of Hawkins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesFictionalPopulation Context triple: [Hawkins General Hospital, servesFictionalPopulation, residents of Hawkins]
-
A.
hasFictionalDemographic
Indicates that an entity is associated with a demographic group that is fictional or exists only within a created narrative or imagined context.
-
B.
fictionalPopulation
chosen
Indicates that a location or setting has an imagined or non-real population, as found in fictional works.
-
C.
hasFictionalInhabitants
Indicates that a place or setting is inhabited by fictional or imaginary beings.
-
D.
worksAtFictionalPlace
Indicates that an entity is employed at or associated with performing work in a fictional or imaginary location.
-
E.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c117ac8190a38c01c3191beaea |
completed | April 20, 2026, 2:10 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7499a4819082bec0be8afba35c |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:38 p.m.