Triple
T35309047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raleigh St. Clair |
E1019714
|
entity |
| Predicate | isMarriedIn |
P35908
|
FINISHED |
| Object | The Royal Tenenbaums |
—
|
NE NERFINISHED |
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: The Royal Tenenbaums | Statement: [Raleigh St. Clair, isMarriedIn, The Royal Tenenbaums]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isMarriedIn Context triple: [Raleigh St. Clair, isMarriedIn, The Royal Tenenbaums]
-
A.
marriedIn
chosen
Indicates that two entities entered into a marital relationship at a specific place or within a particular jurisdiction.
-
B.
hasMarriage
Indicates a marital relationship exists between the two entities, specifying that they are or were legally married to each other.
-
C.
isFianceeOf
Indicates that one person is the engaged-to-be-married partner of another person.
-
D.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
-
E.
marries
Indicates that one entity enters into a legally or socially recognized marital union with 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_69f76de8b4c48190ae504b86185c474c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79052ed048190afc63b2c29b9758b |
completed | May 3, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69f78e2f52e08190a77661223a96c601 |
completed | May 3, 2026, 6:04 p.m. |
Created at: May 3, 2026, 4:03 p.m.