Triple
T10326548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ethel Marion Foreman |
E242777
|
entity |
| Predicate | marriageOrderRelativeToSpouse |
P4764
|
FINISHED |
| Object | first wife of Basil Rathbone |
—
|
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: first wife of Basil Rathbone | Statement: [Ethel Marion Foreman, marriageOrderRelativeToSpouse, first wife of Basil Rathbone]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageOrderRelativeToSpouse Context triple: [Ethel Marion Foreman, marriageOrderRelativeToSpouse, first wife of Basil Rathbone]
-
A.
spouseOrder
chosen
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
B.
predecessorInMaoMarriageOrder
Indicates that one person is immediately earlier than another in the ordered sequence of spouses within a Mao-style marriage arrangement.
-
C.
positionOnMarriage
Indicates a person's stance, opinion, or policy regarding the institution or practice of marriage.
-
D.
marriedToRank
Indicates that one entity is married to another entity who holds a specific rank or position.
-
E.
maritalRelations
Indicates a legally or socially recognized spousal relationship or marriage-based connection between two entities.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ce69b881909f27d97c90643634 |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f64a648190a79980d647898eb0 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:51 a.m.