Triple
T25974920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret DeVogelaere |
E645909
|
entity |
| Predicate | spouseOrderWithRespectToPeterFonda |
P4764
|
FINISHED |
| Object | third wife |
—
|
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: third wife | Statement: [Margaret DeVogelaere, spouseOrderWithRespectToPeterFonda, third wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOrderWithRespectToPeterFonda Context triple: [Margaret DeVogelaere, spouseOrderWithRespectToPeterFonda, third wife]
-
A.
endTimeOfMarriageWithPeterFonda
Indicates the date and time at which a person's marriage to Peter Fonda ended.
-
B.
spouseOrder
chosen
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
C.
spouseOrderRelativeToGeneRoddenberry
Indicates the position or sequence of a person among Gene Roddenberry’s spouses (e.g., first spouse, second spouse, etc.).
-
D.
motherSpouseOrder
Indicates that the subject is the spouse of the object’s mother, with an ordering or ranking among multiple such spouses.
-
E.
marriageOrderRelativeToJasonRobards
Indicates the position or sequence of a marriage in relation to Jason Robards’ own marriages (e.g., earlier, later, or specific ordinal order).
- 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_69e77e8768648190b27bb578f14bcb88 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f67f0488bc819089fbd2d2478158d3 |
completed | May 2, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f67e3ed894819094c067c1ef624951 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 22, 2026, 8:51 a.m.