Triple
T35925002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Lady of Illinois |
E1038994
|
entity |
| Predicate | isUsuallyMarriedTo |
P184234
|
FINISHED |
| Object | sitting Governor of Illinois |
—
|
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: sitting Governor of Illinois | Statement: [First Lady of Illinois, isUsuallyMarriedTo, sitting Governor of Illinois]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isUsuallyMarriedTo Context triple: [First Lady of Illinois, isUsuallyMarriedTo, sitting Governor of Illinois]
-
A.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
-
B.
isFianceeOf
Indicates that one person is the engaged-to-be-married partner of another person.
-
C.
marriedIn
Indicates that two entities entered into a marital relationship at a specific place or within a particular jurisdiction.
-
D.
marriesFor
Indicates that one entity enters into marriage with another entity specifically for a particular reason, motive, or benefit.
-
E.
resultedInMarriageTo
Indicates that one event, action, or circumstance led to or caused a marriage to occur between the related entities.
- F. None of above. chosen
Provenance (4 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_69f76e2320748190b7f5c4750d0cd0d3 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7acaec1508190a38f2ac9cc5383e7 |
completed | May 3, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69f7ab734d848190a84f9b8c3a952b75 |
completed | May 3, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69f7ac2210e481909279dade5328825c |
completed | May 3, 2026, 8:12 p.m. |
Created at: May 3, 2026, 4:07 p.m.