Triple
T35136977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | River City public library |
E1014598
|
entity |
| Predicate | settingForScene |
P171710
|
FINISHED |
| Object | scenes involving Marian Paroo and Harold Hill |
—
|
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: scenes involving Marian Paroo and Harold Hill | Statement: [River City public library, settingForScene, scenes involving Marian Paroo and Harold Hill]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingForScene Context triple: [River City public library, settingForScene, scenes involving Marian Paroo and Harold Hill]
-
A.
isSettingOfScene
chosen
Indicates that a particular location, time, or environment serves as the backdrop or context in which a scene takes place.
-
B.
setting
Indicates the place, time, or context in which an event, action, or interaction occurs.
-
C.
showsScene
Indicates that one entity (such as a media item or visual representation) depicts or presents a particular scene.
-
D.
scenes
Indicates that one entity is a scene or setting in which the other entity occurs, appears, or is depicted.
-
E.
partOfScene
Indicates that one entity functions as a component or element within a larger scene or setting involving another entity.
- 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_69f76dd9c1848190af70d4882a2c1ad7 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fd474b7e788190a9bb9b542d878f60 |
completed | May 8, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69fd46d8b2f0819099d92d72c902f60e |
completed | May 8, 2026, 2:13 a.m. |
Created at: May 3, 2026, 4:02 p.m.