Triple
T17748699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Lo |
E443053
|
entity |
| Predicate | stuntSpecialty |
P87264
|
FINISHED |
| Object | hand-to-hand combat |
—
|
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: hand-to-hand combat | Statement: [Ken Lo, stuntSpecialty, hand-to-hand combat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stuntSpecialty Context triple: [Ken Lo, stuntSpecialty, hand-to-hand combat]
-
A.
hasStunts
Indicates that one entity performs, includes, or is associated with stunt actions for another entity or context.
-
B.
ridingSpecialty
Indicates that one entity has a particular area of expertise or focus related to riding (e.g., a specific riding style, discipline, or type).
-
C.
hasStuntDouble
Indicates that one entity serves as a stunt double who performs dangerous or physically demanding actions on behalf of another entity.
-
D.
escapeSpecialty
Indicates that one entity leaves, avoids, or breaks free from a particular specialized role, field, or area of expertise associated with another entity.
-
E.
skilledIn
chosen
Indicates that an entity possesses ability, expertise, or proficiency in performing or using another entity (such as a task, tool, or domain).
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47ad46a50819089c87f74efe3c7ca |
completed | April 19, 2026, 6:48 a.m. |
| PD | Predicate disambiguation | batch_69e3cde9dc288190af0e2198487f2051 |
completed | April 18, 2026, 6:31 p.m. |
Created at: April 10, 2026, 10:10 a.m.