Triple
T8957965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Winslow |
E213523
|
entity |
| Predicate | relationshipToRonnieWinslow |
P85874
|
FINISHED |
| Object | sister |
—
|
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: sister | Statement: [Catherine Winslow, relationshipToRonnieWinslow, sister]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToRonnieWinslow Context triple: [Catherine Winslow, relationshipToRonnieWinslow, sister]
-
A.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
B.
relationshipToHannah
Indicates the specific type of relationship or connection that an entity has to Hannah.
-
C.
relationshipToSophie
Indicates the specific type of personal or social connection that an entity has to Sophie.
-
D.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
-
E.
relationshipToVeronika
Indicates the specific type of personal, social, or familial relationship that one entity has to Veronika.
- 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_69ca8399ad2081909f8fa41d4314c215 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6729ab7c8190a6168f0aa70a5520 |
completed | April 1, 2026, 12:30 a.m. |
| PD | Predicate disambiguation | batch_69cc5ed74d288190b712d739805579dc |
completed | March 31, 2026, 11:55 p.m. |
| PDg | Predicate description generation | batch_69cc5f887108819096d45a186fc137b3 |
completed | March 31, 2026, 11:58 p.m. |
Created at: March 30, 2026, 7 p.m.