Triple
T30001541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Consultant |
E762180
|
entity |
| Predicate | plotSubject |
P103532
|
FINISHED |
| Object | fate of Emil Blonsky |
—
|
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: fate of Emil Blonsky | Statement: [The Consultant, plotSubject, fate of Emil Blonsky]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: plotSubject Context triple: [The Consultant, plotSubject, fate of Emil Blonsky]
-
A.
depictedSubject
Indicates that one entity visually represents or portrays another entity as its subject in an image or depiction.
-
B.
subjectOfFilm
Indicates that a person, character, or topic is the main focus or central topic depicted in a particular film.
-
C.
plotCharacter
Indicates a relationship where a character plays a role or participates in the narrative plot of a story or work.
-
D.
plotInvolvement
Indicates that an entity participates in, contributes to, or is affected by the events or storyline of a narrative work.
-
E.
notableStorySubject
chosen
Indicates that the subject is a prominent or central topic, character, or element within a particular story or narrative.
- 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_69f2246a47ac81909cf5213053687ffc |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6794eecb48190a679439c69a17137 |
completed | May 2, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69f66ec9919881908a187bfc7c4df192 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:41 p.m.