Triple
T17724759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Venus Presenting Helen to Paris |
E442431
|
entity |
| Predicate | depictsCharacterRole |
P17608
|
FINISHED |
| Object | Venus as matchmaker |
—
|
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: Venus as matchmaker | Statement: [Venus Presenting Helen to Paris, depictsCharacterRole, Venus as matchmaker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsCharacterRole Context triple: [Venus Presenting Helen to Paris, depictsCharacterRole, Venus as matchmaker]
-
A.
depictsPersonRole
chosen
Indicates that an image or representation shows a person in a specific role, function, or capacity.
-
B.
depictsCharacterType
Indicates that one entity visually represents or portrays a character of a specified type or role.
-
C.
featuresCharacterRole
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
D.
depictsPerson
Indicates that one entity visually represents or portrays a specific person.
-
E.
plotCharacter
Indicates a relationship where a character plays a role or participates in the narrative plot of a story or work.
- 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_69d8b9ec79688190b86bdcef85a7b3aa |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47489c59c8190bbffefad20dc6346 |
completed | April 19, 2026, 6:22 a.m. |
| PD | Predicate disambiguation | batch_69e3cde815e08190881972e2d80d151e |
completed | April 18, 2026, 6:31 p.m. |
Created at: April 10, 2026, 10:07 a.m.