Triple
T28518025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seth Green as young Richie Tozier |
E721677
|
entity |
| Predicate | portraysAgeVersionOfCharacter |
P82001
|
FINISHED |
| Object | young Richie Tozier |
—
|
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: young Richie Tozier | Statement: [Seth Green as young Richie Tozier, portraysAgeVersionOfCharacter, young Richie Tozier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysAgeVersionOfCharacter Context triple: [Seth Green as young Richie Tozier, portraysAgeVersionOfCharacter, young Richie Tozier]
-
A.
portraysYoungerVersionOfCharacterFrom
chosen
Indicates that one character is depicted as a younger version of another character from a specified source.
-
B.
portrayedByCharacterAgeApprox
Indicates that an entity is portrayed by a character whose age is approximately a specified value or age range.
-
C.
portraysFromAge
Indicates that one entity depicts another entity starting from a specified age of the depicted entity.
-
D.
portrayalAge
Indicates the age or life stage at which an entity is depicted or represented in a given context.
-
E.
youngerVersionPortrayedBy
Indicates that one person portrays a younger version of another person, typically in a film, television show, or similar narrative work.
- 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_69f01a5cbcc4819083fb4e723378713e |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69fb3425666081908916fcbf3b5dd907 |
completed | May 6, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69fb2f5f3164819099429c2cc3d24e01 |
completed | May 6, 2026, 12:09 p.m. |
Created at: April 28, 2026, 3:18 a.m.