Triple
T10047412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jacob Kershner |
E207650
|
entity |
| Predicate | relationshipToEmmaGoldman |
P91819
|
FINISHED |
| Object | first husband |
—
|
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 husband | Statement: [Jacob Kershner, relationshipToEmmaGoldman, first husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToEmmaGoldman Context triple: [Jacob Kershner, relationshipToEmmaGoldman, first husband]
-
A.
partnerInRevolution
Indicates that two or more entities collaborated as allies or co-participants in the same revolutionary movement or uprising.
-
B.
relationshipToDianaGoodman
Indicates a specified type of relationship or connection that an entity has to Diana Goodman.
-
C.
hasPoliticalRelationshipWith
Indicates a political connection or association between two entities, such as alliances, rivalries, collaborations, or other forms of political interaction.
-
D.
relationshipToNatalieGoodman
Indicates the specific type of personal or social relationship an entity has with Natalie Goodman.
-
E.
notableAlly
Indicates that one entity is recognized as a significant or distinguished ally of another entity.
- 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_69ca835ad0608190b7c80b292da004f5 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcf664dd881908786fcd802bf10da |
completed | April 2, 2026, 2:07 a.m. |
| PD | Predicate disambiguation | batch_69cd4b8d2280819089de27e57babd1f3 |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd4f8d9b888190b8067bd916dae773 |
completed | April 1, 2026, 5:02 p.m. |
Created at: March 30, 2026, 8:56 p.m.