Triple
T29188648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Youngling |
E739935
|
entity |
| Predicate | skillDevelopment |
P98489
|
FINISHED |
| Object | basic Force control |
—
|
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: basic Force control | Statement: [Youngling, skillDevelopment, basic Force control]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: skillDevelopment Context triple: [Youngling, skillDevelopment, basic Force control]
-
A.
developsSkill
chosen
Indicates that one entity causes or contributes to the growth, improvement, or acquisition of a particular skill in another entity.
-
B.
skillSet
Indicates that an entity possesses or is associated with a particular collection of skills or competencies.
-
C.
skillType
Indicates the specific category or kind of skill that characterizes or classifies an associated skill-related entity or action.
-
D.
skillTaught
Indicates that one entity teaches or imparts a particular skill to another entity.
-
E.
skillEmphasis
Indicates that a particular skill is given special focus, priority, or importance within a context such as a role, task, or curriculum.
- 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_69f66388afe48190a68caf56c9745007 |
completed | May 2, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69f65c24f8b48190af81b575f3c15be5 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 28, 2026, 12:01 p.m.