Triple
T31445826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Story Book Dining at Artist Point with Snow White |
E802183
|
entity |
| Predicate | characterInteraction |
P196872
|
FINISHED |
| Object | meet-and-greet |
—
|
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: meet-and-greet | Statement: [Story Book Dining at Artist Point with Snow White, characterInteraction, meet-and-greet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterInteraction Context triple: [Story Book Dining at Artist Point with Snow White, characterInteraction, meet-and-greet]
-
A.
heroInteraction
Indicates an interaction or engagement occurring between a hero and another entity, such as assisting, confronting, or collaborating.
-
B.
characterDuet
Indicates a relationship where two characters perform together as a duet in a shared scene, song, or action.
-
C.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
D.
characterReveals
Indicates that one character discloses or makes known information, feelings, or intentions to another character.
-
E.
discussedByCharacter
Indicates that a topic, event, or subject is talked about or examined in dialogue or thought by a character.
- F. None of above. chosen
Provenance (4 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_69f348c5a6bc819092a557e95438976f |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe6c811bcc81908b1e1b1f8bcb071b |
completed | May 8, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69fe6c026d5481908b7a814dcf38c183 |
completed | May 8, 2026, 11:04 p.m. |
| PDg | Predicate description generation | batch_69fe6c7fc4388190aa88993d00872d7f |
completed | May 8, 2026, 11:06 p.m. |
Created at: April 30, 2026, 9:09 p.m.