Triple
T4063546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I like to see it lap the Miles |
E86270
|
entity |
| Predicate | portraysTrainAs |
P53847
|
FINISHED |
| Object | powerful creature |
—
|
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: powerful creature | Statement: [I like to see it lap the Miles, portraysTrainAs, powerful creature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysTrainAs Context triple: [I like to see it lap the Miles, portraysTrainAs, powerful creature]
-
A.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
-
B.
trainsForOccupation
Indicates that an entity undergoes training or preparation aimed at qualifying for or performing a specific occupation.
-
C.
notableTrain
Indicates that there is a train or rail service associated with the subject that is considered notable or significant in some way.
-
D.
providesTrainingFor
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
E.
trainsArtistsFor
Indicates a relationship where one entity provides instruction or guidance to prepare another entity to become or work as an artist.
- F. None of above. chosen
Provenance (4 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_69aed93c69208190a4efac0efe3cd69b |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefd0bdea48190805a79515ee92709 |
completed | March 9, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69aef90438908190a005b08ba271eacf |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefd0995188190b1bc8771fe7f423a |
completed | March 9, 2026, 5:02 p.m. |
Created at: March 9, 2026, 3:38 p.m.