Triple
T24834241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pawnee Parks and Recreation Department |
E621422
|
entity |
| Predicate | fictionalAddressState |
P115061
|
FINISHED |
| Object | Indiana |
—
|
NE NERFINISHED |
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: Indiana | Statement: [Pawnee Parks and Recreation Department, fictionalAddressState, Indiana]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalAddressState Context triple: [Pawnee Parks and Recreation Department, fictionalAddressState, Indiana]
-
A.
stateOrTerritory
Indicates that one entity is a state or territory that is politically or administratively associated with another entity.
-
B.
provinceState
Indicates that one entity is a province or state that is administratively contained within or associated with another, typically larger, territorial entity.
-
C.
stateName
Indicates that an entity is identified by or associated with a particular state’s name.
-
D.
stateOrRegion
chosen
Indicates that one entity is a state or region in which the other entity is located or with which it is associated.
-
E.
provinceName
Indicates that a province entity is associated with its specific name as a textual label.
- 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_69e2fac185d48190a0a6073ad1f6b792 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f5f6baf2d48190a6a4cd6501be87d2 |
completed | May 2, 2026, 1:06 p.m. |
| PD | Predicate disambiguation | batch_69f5afd5baac8190bb8ed576813c8591 |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 18, 2026, 5:17 a.m.