Triple
T9473710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Goodman |
E228456
|
entity |
| Predicate | relationshipToDianaGoodman |
P88310
|
FINISHED |
| Object | primary caregiver and emotional support |
—
|
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: primary caregiver and emotional support | Statement: [Dan Goodman, relationshipToDianaGoodman, primary caregiver and emotional support]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToDianaGoodman Context triple: [Dan Goodman, relationshipToDianaGoodman, primary caregiver and emotional support]
-
A.
relationshipTypeWith Alicia Johns
Indicates the specific type or nature of the relationship that an entity has with Alicia Johns.
-
B.
relationshipToDickieWinslow
Indicates the specific type of relationship or connection an entity has to Dickie Winslow.
-
C.
relationshipToDonnaSheridan
Indicates the specific type of personal or familial connection an entity has with Donna Sheridan.
-
D.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
E.
relationshipToMargoChanning
Indicates the nature or type of relationship an entity has with Margo Channing.
- 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_69ca847162c48190b079076c9595513c |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7ff23aec8190b5341eb9bd816a43 |
completed | April 1, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69cca55f01b081908dc0f12eaa45f832 |
completed | April 1, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69cca89d0f0c8190b4528990fe708fca |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 7:54 p.m.