Triple
T28672774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlie McGee |
E725772
|
entity |
| Predicate | hasOrganizationAsAntagonist |
P18963
|
FINISHED |
| Object | The Shop |
—
|
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: The Shop | Statement: [Charlie McGee, hasOrganizationAsAntagonist, The Shop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOrganizationAsAntagonist Context triple: [Charlie McGee, hasOrganizationAsAntagonist, The Shop]
-
A.
hasAntagonistGroup
Indicates that an entity is opposed or challenged by a specific group acting as its antagonist.
-
B.
usedByAntagonistOrganization
Indicates that something (such as an object, resource, method, or location) is employed or exploited by an antagonist organization for its purposes.
-
C.
hasAntagonisticProtagonist
Indicates that the work features a main character who opposes or undermines the typical heroic or moral expectations of a traditional protagonist.
-
D.
villainOrganization
Indicates that an entity is an organization characterized as antagonistic, criminal, or evil within a given context or narrative.
-
E.
antagonistOf
chosen
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to 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_69f01d85be388190b669a0e401e2f2c4 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
Created at: April 28, 2026, 5:04 a.m.