Triple
T6390837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benny Kornegay |
E143820
|
entity |
| Predicate | marriageOrderWithEllaFitzgerald |
P4764
|
FINISHED |
| Object | second husband |
—
|
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: second husband | Statement: [Benny Kornegay, marriageOrderWithEllaFitzgerald, second husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageOrderWithEllaFitzgerald Context triple: [Benny Kornegay, marriageOrderWithEllaFitzgerald, second husband]
-
A.
marriedToBeforeFameOf
Indicates that one person was married to another person before the latter became famous.
-
B.
marriageSequenceToHussein
Indicates that an entity enters into a marriage with Hussein at a specific point or order within a sequence of marriages.
-
C.
marriageResolvedBy
Indicates that a marital relationship between two parties has been formally concluded or dissolved through a specific resolving action or process (e.g., divorce, annulment).
-
D.
spouseOrder
chosen
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
E.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of 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_69c008db906c819096f3597d55d95432 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0687c7fdc819099cbbb937dec215e |
completed | March 22, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69c060f25c088190b433f78553ff1d84 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:34 p.m.