Triple
T16007985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Lamb |
E388265
|
entity |
| Predicate | showsSkill |
P96902
|
FINISHED |
| Object | applying makeup for films |
—
|
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: applying makeup for films | Statement: [Joe Lamb, showsSkill, applying makeup for films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: showsSkill Context triple: [Joe Lamb, showsSkill, applying makeup for films]
-
A.
indicatesSkill
chosen
Indicates a relationship where one entity possesses, demonstrates, or is associated with a particular skill represented by another entity.
-
B.
skillTaught
Indicates that one entity teaches or imparts a particular skill to another entity.
-
C.
skilledIn
Indicates that an entity possesses ability, expertise, or proficiency in performing or using another entity (such as a task, tool, or domain).
-
D.
hasEponymousSkill
Indicates that an entity possesses a skill that is named after a particular person or entity.
-
E.
learnsSkill
Indicates that an entity acquires or develops a particular skill through learning or practice.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142dc081c819082527e3fa8773460 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:55 a.m.