Triple
T837542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Without Tears |
E18102
|
entity |
| Predicate | mainCharacters |
P9202
|
FINISHED |
| Object | young adults |
—
|
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: young adults | Statement: [French Without Tears, mainCharacters, young adults]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCharacters Context triple: [French Without Tears, mainCharacters, young adults]
-
A.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
B.
mainProtagonist
chosen
Indicates that the subject is the central character or primary focus in the narrative of the related work.
-
C.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
D.
supportingCharacter
Indicates that one entity plays a secondary or assisting role in the story or context relative to another primary entity.
-
E.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
- 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abcf69888190b342363978273ae2 |
completed | March 1, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7dfc5c8190890c9df485d73a86 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.