Triple
T6091753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Things You Can Tell Just by Looking at Her |
E135780
|
entity |
| Predicate | hasVignetteStructure |
P45542
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Things You Can Tell Just by Looking at Her, hasVignetteStructure, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVignetteStructure Context triple: [Things You Can Tell Just by Looking at Her, hasVignetteStructure, yes]
-
A.
hasVignette
chosen
Indicates that one entity possesses, includes, or is associated with a vignette (such as a brief scene, illustration, or decorative element).
-
B.
vignetteRequiredFor
Indicates that a vignette (such as a permit, sticker, or documentation) is required in order for something to be used, accessed, or performed.
-
C.
hasHumanStructure
Indicates that one entity possesses or exhibits a structural form or organization characteristic of humans.
-
D.
hasIconicStructure
Indicates that an entity possesses a structure or feature that is widely recognized as emblematic or symbolically representative of it.
-
E.
hasVariant
Indicates that one entity exists as an alternative form, version, or variation of another 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057ab7324819086d4708e6f9391c0 |
completed | March 22, 2026, 8:57 p.m. |
| PD | Predicate disambiguation | batch_69c049f3b1ec8190bea67a7bec6442a5 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:12 p.m.