Triple
T25118107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shelly Pfefferman |
E629187
|
entity |
| Predicate | relationshipToEdPaskowitz |
P129828
|
FINISHED |
| Object | second husband’s caregiver and later wife |
—
|
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: second husband’s caregiver and later wife | Statement: [Shelly Pfefferman, relationshipToEdPaskowitz, second husband’s caregiver and later wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToEdPaskowitz Context triple: [Shelly Pfefferman, relationshipToEdPaskowitz, second husband’s caregiver and later wife]
-
A.
relationshipToEd
chosen
Indicates the specific type of relationship or connection that an entity has to Ed.
-
B.
relationshipToEdd
Indicates the specific type of relationship or connection that an entity has to Edd.
-
C.
relationshipToPavelVlasov
Indicates the nature or type of relationship an entity has with Pavel Vlasov.
-
D.
relationshipToPaul
Indicates a specified type of personal or social relationship that an entity has with Paul.
-
E.
relationshipToPolinaAlexandrovna
Indicates the specific type of personal or social relationship that one entity has with Polina Alexandrovna.
- 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_69e2ff3169d08190973b6061d5009abd |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f969b4cc8190afb473a2d8b110bc |
completed | May 3, 2026, 7:29 a.m. |
Created at: April 18, 2026, 6:27 a.m.