Triple
T8503798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tim Minchin |
E201284
|
entity |
| Predicate | eyeMakeup |
P78811
|
FINISHED |
| Object | heavy eyeliner on stage |
—
|
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: heavy eyeliner on stage | Statement: [Tim Minchin, eyeMakeup, heavy eyeliner on stage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eyeMakeup Context triple: [Tim Minchin, eyeMakeup, heavy eyeliner on stage]
-
A.
eyeType
Indicates the specific kind or category of eyes an entity has, such as their form, structure, or visual style.
-
B.
makeupType
chosen
Indicates the specific kind or category of makeup associated with an entity.
-
C.
eyeCharacteristic
Indicates a relationship where an entity possesses a specific attribute, feature, or quality of its eyes.
-
D.
eyeAssociation
Indicates a relationship where one entity is associated with, linked to, or characterized by a particular eye or eye-related feature of another entity.
-
E.
eyePatch
Indicates that one entity wears or has an eye patch, typically covering one of their eyes.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe59d67d081908155a43b9b463fe3 |
completed | March 31, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cbd10cfd208190a519049fad32c508 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:14 p.m.