Triple
T5367795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pecola Breedlove |
E103171
|
entity |
| Predicate | fictionalSettingTime |
P11197
|
FINISHED |
| Object | 1940s |
—
|
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: 1940s | Statement: [Pecola Breedlove, fictionalSettingTime, 1940s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalSettingTime Context triple: [Pecola Breedlove, fictionalSettingTime, 1940s]
-
A.
fictionalTime
Indicates that the associated time or temporal reference exists only within a fictional or imagined context, rather than in real-world chronology.
-
B.
fictionalEra
Indicates the time period or age within a fictional or imaginary setting in which an entity exists or an event occurs.
-
C.
fictionalAge
Indicates the age attributed to an entity within a fictional or narrative context, rather than its real-world age.
-
D.
setInFictionalYear
Indicates that the events or narrative of a work are situated in a specified fictional or non-real calendar year.
-
E.
timeOfNarrative
chosen
Indicates the specific time or period during which the events of a narrative are set or unfold.
- 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_69bd43daa3e4819090b59d127db70e57 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd86856f688190a34ab93619bae134 |
completed | March 20, 2026, 5:40 p.m. |
| PD | Predicate disambiguation | batch_69bd845f41f88190b75b8b64b9e41862 |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:02 p.m.