Triple
T870827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vivian Shapiro |
E18807
|
entity |
| Predicate | marriedToPositionHolderOf |
P4763
|
FINISHED |
| Object | President of Princeton University |
—
|
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: President of Princeton University | Statement: [Vivian Shapiro, marriedToPositionHolderOf, President of Princeton University]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToPositionHolderOf Context triple: [Vivian Shapiro, marriedToPositionHolderOf, President of Princeton University]
-
A.
firstHolderSpouseOf
Indicates that the first holder in the relation is the spouse (married partner) of the other holder.
-
B.
metSpouseThrough
Indicates that one person became acquainted with and subsequently married their spouse as a result of a particular intermediary person, event, place, or context.
-
C.
positionHeldBySpouse
chosen
Indicates that a particular position, role, or office is or was held by the spouse of a given person.
-
D.
positionOnMarriage
Indicates a person's stance, opinion, or policy regarding the institution or practice of marriage.
-
E.
marriedInto
Indicates that one entity became connected to another’s family or group through marriage, rather than by birth or prior membership.
- 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac94d5ac81909feee876696da589 |
completed | March 1, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69a4aa89ca008190b50d061ac7fe19f9 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.