Triple
T37923642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marmaduke Bonthrop Shelmerdine |
E946031
|
entity |
| Predicate | protagonistOfRelationshipWith |
P27138
|
FINISHED |
| Object | Orlando |
—
|
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: Orlando | Statement: [Marmaduke Bonthrop Shelmerdine, protagonistOfRelationshipWith, Orlando]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: protagonistOfRelationshipWith Context triple: [Marmaduke Bonthrop Shelmerdine, protagonistOfRelationshipWith, Orlando]
-
A.
hasProtagonistRelationship
chosen
Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a narrative.
-
B.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
-
C.
protagonistLover
Indicates that one entity is the romantic partner or love interest of the story’s protagonist.
-
D.
sexualRelationshipTo
Indicates that one entity has engaged in a sexual relationship or sexual activity with another entity.
-
E.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
- 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_69f76ef3b7248190892fb9706423be7c |
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.