Triple
T23538284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chuck Baxter |
E577666
|
entity |
| Predicate | relationshipToFranKubelik |
P152716
|
FINISHED |
| Object | falls in love with Fran Kubelik |
—
|
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: falls in love with Fran Kubelik | Statement: [Chuck Baxter, relationshipToFranKubelik, falls in love with Fran Kubelik]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToFranKubelik Context triple: [Chuck Baxter, relationshipToFranKubelik, falls in love with Fran Kubelik]
-
A.
relationshipToKeter
Indicates a relationship in which an entity is connected or related to the concept, object, or category referred to as "Keter."
-
B.
relationshipToPolinaAlexandrovna
Indicates the specific type of personal or social relationship that one entity has with Polina Alexandrovna.
-
C.
relationshipToBéralde
Indicates the type or nature of a person or entity’s relationship to Béralde.
-
D.
unitRelation
Indicates a relationship between units, such as how one unit is associated with, derived from, or converted to another.
-
E.
relationshipToAligoté
Indicates the specific type of relationship or connection an entity has to Aligoté (typically a wine grape or wine style).
- 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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ae19473881909aa65f9d36744502 |
completed | April 29, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69f118afabd88190bd88f49597d120e8 |
completed | April 28, 2026, 8:29 p.m. |
| PDg | Predicate description generation | batch_69f121cc494081908c987adfcde89b0e |
completed | April 28, 2026, 9:08 p.m. |
Created at: April 17, 2026, 6:10 p.m.