Triple
T9122701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zulu Army |
E218892
|
entity |
| Predicate | trainingFeature |
P32757
|
FINISHED |
| Object | long-distance running endurance |
—
|
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: long-distance running endurance | Statement: [Zulu Army, trainingFeature, long-distance running endurance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingFeature Context triple: [Zulu Army, trainingFeature, long-distance running endurance]
-
A.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
B.
trainingCharacteristic
chosen
Indicates that an entity has a specific property, feature, or quality related to training (such as method, intensity, or style).
-
C.
trainingComponent
Indicates that one entity functions as a training-related part, module, or element within a larger training process or system involving another entity.
-
D.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
E.
trainerModel
Indicates that one entity serves as the trainer or training source for a model 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_69ca83dddd548190983b96c664f7f367 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8b46dac8190bde88205c85f596a |
completed | April 1, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69cc66003e3c819091e1e42c9cf7c781 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:17 p.m.