Triple
T12530624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L'Appartement |
E299552
|
entity |
| Predicate | hasLoveStoryElement |
P19974
|
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: [L'Appartement, hasLoveStoryElement, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLoveStoryElement Context triple: [L'Appartement, hasLoveStoryElement, yes]
-
A.
hasLoveLifeCharacteristic
Indicates that an entity possesses a particular quality, status, or attribute related to its romantic or love life.
-
B.
hasMarriagePlot
chosen
Indicates that the work’s narrative centrally involves courtship, romantic relationships, or the progression toward marriage as a key plot element.
-
C.
hasAllyInStory
Indicates that one entity is portrayed as an ally or supportive partner of another entity within the context of a specific story or narrative.
-
D.
hasFictionalBeloved
Indicates that an entity has a romantic partner or beloved who exists only as a fictional character.
-
E.
hasWeddingSceneWith
Indicates that two entities appear together in a wedding scene within the same context 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95f5507b481908d13cc317b7402f6 |
completed | April 10, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69d9540d7b788190a0d57b098e90e491 |
completed | April 10, 2026, 7:48 p.m. |
Created at: April 8, 2026, 9:57 p.m.