Triple
T37749489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Genzlinger |
E940938
|
entity |
| Predicate | hasRomanticStatusWithJessicaDay |
P198618
|
FINISHED |
| Object | ex-boyfriend |
—
|
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: ex-boyfriend | Statement: [Paul Genzlinger, hasRomanticStatusWithJessicaDay, ex-boyfriend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanticStatusWithJessicaDay Context triple: [Paul Genzlinger, hasRomanticStatusWithJessicaDay, ex-boyfriend]
-
A.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
B.
hasRomanticEncounterWith
Indicates that two entities engage in or share a romantic or intimate encounter with each other.
-
C.
relationshipToLadyJessica
Indicates the specific type of personal or familial relationship an entity has with Lady Jessica.
-
D.
hasFictionalRomanticInterest
Indicates that one entity is portrayed as having a romantic attraction or interest toward another entity within a fictional context.
-
E.
relationshipTypeWith Jessa Johansson
Indicates the specific nature or category of relationship that an entity has with Jessa Johansson.
- 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_69f76ee1f3a88190834e6c8af99bccc9 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fef5cf8da881908260ec633830375d |
completed | May 9, 2026, 8:52 a.m. |
| PD | Predicate disambiguation | batch_69fef455e40481909861c82007b79bc0 |
completed | May 9, 2026, 8:46 a.m. |
| PDg | Predicate description generation | batch_69fef5cec8208190b85665ab6a511a08 |
completed | May 9, 2026, 8:52 a.m. |
Created at: May 3, 2026, 4:19 p.m.