Triple
T33606117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorothy Bonvillion |
E860861
|
entity |
| Predicate | marriagePeriodRelativeToCareer |
P113644
|
FINISHED |
| Object | before George Jones's rise to fame |
—
|
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: before George Jones's rise to fame | Statement: [Dorothy Bonvillion, marriagePeriodRelativeToCareer, before George Jones's rise to fame]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriagePeriodRelativeToCareer Context triple: [Dorothy Bonvillion, marriagePeriodRelativeToCareer, before George Jones's rise to fame]
-
A.
marriagePeriodWith
Indicates the time span during which two entities were married to each other.
-
B.
marriagePeriodRelative
chosen
Indicates the time span of a marriage expressed relative to some reference point or period rather than as absolute dates.
-
C.
preMarriageOccupation
Indicates the occupation or job role a person held before getting married.
-
D.
marriageStatusInWork
Indicates the marital status a person has within the context of a specific work (e.g., story, film, or document), which may differ from their real-life or external marital status.
-
E.
maritalPeriodWith
Indicates the time span during which two entities were married to each other.
- 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_69f3498037c88190a4500f002b5540e0 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fd4129a8848190a5002150278ac689 |
completed | May 8, 2026, 1:49 a.m. |
| PD | Predicate disambiguation | batch_69fd3e0515ec8190937c7af71ebc3875 |
completed | May 8, 2026, 1:36 a.m. |
Created at: May 1, 2026, 1:41 a.m.