Triple
T7659637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bubba Bexley |
E173470
|
entity |
| Predicate | fictionalUniverseTimePeriod |
P60994
|
FINISHED |
| Object | contemporary to 1970s |
—
|
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: contemporary to 1970s | Statement: [Bubba Bexley, fictionalUniverseTimePeriod, contemporary to 1970s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalUniverseTimePeriod Context triple: [Bubba Bexley, fictionalUniverseTimePeriod, contemporary to 1970s]
-
A.
fictionalEra
Indicates the time period or age within a fictional or imaginary setting in which an entity exists or an event occurs.
-
B.
fictionalTime
chosen
Indicates that the associated time or temporal reference exists only within a fictional or imagined context, rather than in real-world chronology.
-
C.
fictionalUniverse
Indicates that two entities exist within, or are associated with, the same fictional universe or narrative setting.
-
D.
fictionalAge
Indicates the age attributed to an entity within a fictional or narrative context, rather than its real-world age.
-
E.
fictionalUniverseCreated
Indicates that one entity is the creator or originator of a particular fictional universe or setting in which stories or works take place.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7061cbc3c8190a917dd7e71214182 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015dd8fc8190bc5f52a12bd46209 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 3:59 p.m.