Triple
T37989162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgina "Gina" Montana |
E947776
|
entity |
| Predicate | romanticInterestInFiction |
P110564
|
FINISHED |
| Object | Manny Ribera |
—
|
NE NERFINISHED |
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: Manny Ribera | Statement: [Georgina "Gina" Montana, romanticInterestInFiction, Manny Ribera]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: romanticInterestInFiction Context triple: [Georgina "Gina" Montana, romanticInterestInFiction, Manny Ribera]
-
A.
fictionalLover
chosen
Indicates a romantic partner or love interest that exists only within a fictional or imaginary context.
-
B.
romanticSubplotCentral
Indicates that a romantic subplot is a primary, driving element of the narrative rather than a minor or peripheral thread.
-
C.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
D.
romanceType
Indicates the specific kind or category of romantic relationship that exists between the related entities.
-
E.
romanticallyObsessedWith
Indicates a strong, often overwhelming romantic fixation or preoccupation that one entity has toward another.
- 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_69f76ef8a1d08190a741bbbc5970e3b3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbc995dc2481908b3bd4217f8101e7 |
completed | May 6, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69fbc8ee04f08190977b7ad70fc85896 |
completed | May 6, 2026, 11:04 p.m. |
Created at: May 3, 2026, 4:20 p.m.