Triple
T36412010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daisy Mason |
E896904
|
entity |
| Predicate | relationshipTypeWithWilliamMason |
P204863
|
FINISHED |
| Object | marriage shortly before his death |
—
|
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: marriage shortly before his death | Statement: [Daisy Mason, relationshipTypeWithWilliamMason, marriage shortly before his death]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithWilliamMason Context triple: [Daisy Mason, relationshipTypeWithWilliamMason, marriage shortly before his death]
-
A.
relationshipTypeWith Willis Davidge
Indicates the specific nature or category of the relationship that an entity has with Willis Davidge.
-
B.
relationshipToWilliamBloom
Indicates the nature or type of relational connection an entity has with William Bloom.
-
C.
relationshipTypeWithWill Freeman
Indicates the specific nature or category of the relationship that an entity has with Will Freeman.
-
D.
relationshipToWilliamMunny
Indicates the specific familial, social, or interpersonal relationship an entity has with William Munny.
-
E.
relationshipTypeWith William of Orange
Indicates the specific type of interpersonal or familial relationship that an entity has with William of Orange.
- 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_69f76e54ce408190849acc3f7758937c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:10 p.m.