Triple
T29195092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zuko |
E740103
|
entity |
| Predicate | teachesAang |
P61611
|
FINISHED |
| Object | firebending |
—
|
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: firebending | Statement: [Zuko, teachesAang, firebending]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teachesAang Context triple: [Zuko, teachesAang, firebending]
-
A.
learnedBloodbendingFrom
Indicates that one entity acquired the ability or knowledge of bloodbending through instruction or training from another entity.
-
B.
teachesBod
Indicates that one entity provides instruction or teaching to another entity.
-
C.
taughtAs
Indicates that one entity served as a teacher or instructor for another entity in an educational or training context.
-
D.
taughtThat
chosen
Indicates that one entity provided instruction or education to another entity about a specific subject, skill, or concept.
-
E.
teachesCharactersFrom
Indicates that one entity instructs or educates another entity using characters (such as letters, symbols, or written forms) as the teaching content.
- 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_69f07cb8033c8190b8807e219a14333d |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f674e06c9481909ed0ea736408f0d7 |
completed | May 2, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69f673c4abec8190bc2379e66f4af0a9 |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 28, 2026, 12:04 p.m.