Triple
T35014968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Too Young |
E1010028
|
entity |
| Predicate | hasSeriesMainCharacter |
P197057
|
FINISHED |
| Object | Finn the Human |
—
|
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: Finn the Human | Statement: [Too Young, hasSeriesMainCharacter, Finn the Human]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeriesMainCharacter Context triple: [Too Young, hasSeriesMainCharacter, Finn the Human]
-
A.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
-
B.
hasMainThemeCharacter
Indicates that a work (such as a story, film, or game) features a specific character as its central or primary thematic focus.
-
C.
literarySeriesCharacter
Indicates that a character appears in, is part of, or is associated with a particular literary series.
-
D.
laterMainCharacterOf
Indicates that one entity becomes the main character of a work at a later point in time, succeeding another main character.
-
E.
hasFictionalLeadCharacter
chosen
Indicates that a creative work features a particular fictional character as its main or leading protagonist.
- 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_69f76dcc3ac8819096a3ed52f5fa2523 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fff9b126b4819085a4cf8791d388d1 |
completed | May 10, 2026, 3:21 a.m. |
| PD | Predicate disambiguation | batch_69fff8f913a881908d3b7e490d92631f |
completed | May 10, 2026, 3:18 a.m. |
Created at: May 3, 2026, 4:01 p.m.