Triple
T38317808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Ross Jennings |
E1033873
|
entity |
| Predicate | antagonistTypeFaced |
P93370
|
FINISHED |
| Object | deadly arachnids |
—
|
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: deadly arachnids | Statement: [Dr. Ross Jennings, antagonistTypeFaced, deadly arachnids]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: antagonistTypeFaced Context triple: [Dr. Ross Jennings, antagonistTypeFaced, deadly arachnids]
-
A.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
B.
antagonistStatus
Indicates that an entity holds an opposing or adversarial role, often acting as the main source of conflict relative to another entity or objective.
-
C.
antagonistEmbodiment
Indicates that one entity serves as the personification or concrete manifestation of an antagonistic force, role, or concept in relation to another entity.
-
D.
primaryAntagonistType
Indicates the role or category of the main opposing force or adversary that serves as the central source of conflict.
-
E.
facesAntagonistType
chosen
Indicates that an entity confronts or opposes an antagonist of a specified type.
- 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_69f76e132c408190969b3d35c04b87ae |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fcd1499e2c81909bafd84dc4810f45 |
completed | May 7, 2026, 5:52 p.m. |
| PD | Predicate disambiguation | batch_69fcccf024ec819086383ffbb6cfc036 |
completed | May 7, 2026, 5:33 p.m. |
Created at: May 3, 2026, 4:30 p.m.