Triple
T38644685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warrior’s Terrace |
E938684
|
entity |
| Predicate | classTrainerType |
P24898
|
FINISHED |
| Object | warrior |
—
|
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: warrior | Statement: [Warrior’s Terrace, classTrainerType, warrior]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: classTrainerType Context triple: [Warrior’s Terrace, classTrainerType, warrior]
-
A.
hasTrainingType
Indicates that an entity is associated with or characterized by a specific type or category of training.
-
B.
trainsetType
Indicates the specific category or role of a dataset within a training process (e.g., training, validation, or test set).
-
C.
coachType
chosen
Indicates the specific category or role of a coach associated with an entity (e.g., head coach, assistant coach, position coach).
-
D.
alsoTrains
Indicates that an entity, in addition to its primary role or activity, is involved in training another entity.
-
E.
providesTrainingFor
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
- 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_69f76ed948ec81908ce7811608a8f359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: May 3, 2026, 4:32 p.m.