Triple
T24834233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pawnee Parks and Recreation Department |
E621422
|
entity |
| Predicate | hasDivisionFictional |
P163664
|
FINISHED |
| Object | parks maintenance |
—
|
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: parks maintenance | Statement: [Pawnee Parks and Recreation Department, hasDivisionFictional, parks maintenance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDivisionFictional Context triple: [Pawnee Parks and Recreation Department, hasDivisionFictional, parks maintenance]
-
A.
hasFictionalHierarchy
Indicates that one entity occupies a specific level, rank, or position within a fictional or imagined hierarchical structure defined by another entity.
-
B.
hasFictionalType
Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
-
C.
hasFictionalUniverseGenre
Indicates that a fictional universe is associated with a particular genre that characterizes its overall style, themes, or narrative type.
-
D.
hasFictionComponent
Indicates that something includes, contains, or is composed in part of a fictional element or work.
-
E.
hasFictionalScope
Indicates that something pertains to, applies within, or is limited to a fictional or imagined context rather than real-world scope.
- F. None of above. chosen
Provenance (4 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_69e2fac185d48190a0a6073ad1f6b792 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f6397b64f881909d811225e57aac5e |
completed | May 2, 2026, 5:50 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
| PDg | Predicate description generation | batch_69f638d029148190877c103f0eeaf147 |
completed | May 2, 2026, 5:48 p.m. |
Created at: April 18, 2026, 5:17 a.m.