Triple
T23056664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jasmine French |
E574174
|
entity |
| Predicate | relationshipToGinger |
P150806
|
FINISHED |
| Object | adoptive 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: adoptive sister | Statement: [Jasmine French, relationshipToGinger, adoptive sister]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToGinger Context triple: [Jasmine French, relationshipToGinger, adoptive sister]
-
A.
relationshipToHamm
Indicates that one entity stands in a specified relationship to the entity referred to as Hamm.
-
B.
relationshipToEdd
Indicates the specific type of relationship or connection that an entity has to Edd.
-
C.
relationshipToMary
Indicates that one entity stands in a specified personal or social relationship to Mary.
-
D.
relationshipToKenny
Indicates the specific familial, social, or interpersonal connection that one entity has to Kenny.
-
E.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
- 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_69e245ba7ae48190be606dbc54120e39 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1868099708190b23725dc8a305e09 |
completed | April 29, 2026, 4:18 a.m. |
| PD | Predicate disambiguation | batch_69ef89d5f71881908b9f9d0c8aab278c |
completed | April 27, 2026, 4:07 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 3:55 p.m.