Triple
T24852160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jenny |
E621916
|
entity |
| Predicate | hasSettingInFiction |
P116836
|
FINISHED |
| Object | fictional city of Mahagonny |
—
|
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: fictional city of Mahagonny | Statement: [Jenny, hasSettingInFiction, fictional city of Mahagonny]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSettingInFiction Context triple: [Jenny, hasSettingInFiction, fictional city of Mahagonny]
-
A.
hasFeatureInFiction
Indicates that a fictional work includes or portrays a particular feature, trait, or characteristic.
-
B.
hasEventInFiction
Indicates that a fictional work includes or depicts a particular event within its narrative.
-
C.
hasSkillInFiction
Indicates that an entity possesses skill or proficiency in the domain of fiction.
-
D.
laterSettingOfFiction
Indicates that one fictional work is set chronologically later than another within a shared narrative or story world.
-
E.
hasFictionalSettingElement
chosen
Indicates that something includes or is associated with a specific element or component of a fictional setting.
- 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_69e2fac297e481909d3aedc75f585e42 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f606c79ad081908369605f72e65ca6 |
completed | May 2, 2026, 2:14 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 18, 2026, 5:20 a.m.