Triple
T25069182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Werther Fever |
E627861
|
entity |
| Predicate | influencedBehavior |
P9
|
FINISHED |
| Object | posing for portraits dressed as Werther |
—
|
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: posing for portraits dressed as Werther | Statement: [Werther Fever, influencedBehavior, posing for portraits dressed as Werther]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencedBehavior Context triple: [Werther Fever, influencedBehavior, posing for portraits dressed as Werther]
-
A.
influenced
chosen
Indicates that one entity has affected, shaped, or altered another entity’s state, behavior, or characteristics.
-
B.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
-
C.
influencedPerceptionOf
Indicates that one entity has affected, shaped, or altered how another entity is perceived or understood.
-
D.
influencesThrough
Indicates that one entity affects or alters another entity indirectly by means of an intermediate factor, channel, or mechanism.
-
E.
influencedAspectOf
Indicates that one entity has affected, shaped, or altered a particular aspect or component 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_69e2ff2d71dc8190b4758e57d643cbe4 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f6135293908190809e255bf6334760 |
completed | May 2, 2026, 3:08 p.m. |
| PD | Predicate disambiguation | batch_69f611a72780819082f44e66ca2c6ac9 |
completed | May 2, 2026, 3 p.m. |
Created at: April 18, 2026, 6:10 a.m.