Triple
T24157751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Snegina |
E598734
|
entity |
| Predicate | hasRomanticPlotline |
P154818
|
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: [Anna Snegina, hasRomanticPlotline, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanticPlotline Context triple: [Anna Snegina, hasRomanticPlotline, yes]
-
A.
hasRomanticSubplot
Indicates that a work includes a secondary storyline centered on a romantic relationship between characters.
-
B.
hasRomanticSceneAt
Indicates that a romantic scene occurs at a specific location or point in time within a work or context.
-
C.
romanticSubplotCentral
Indicates that a romantic subplot is a primary, driving element of the narrative rather than a minor or peripheral thread.
-
D.
hasRomanticMisadventures
Indicates that an entity experiences a series of problematic, comical, or unsuccessful romantic relationships or encounters.
-
E.
hasMarriagePlot
Indicates that the work’s narrative centrally involves courtship, romantic relationships, or the progression toward marriage as a key plot element.
- 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_69e288cb0a3081909ef221744f274384 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1e0e6d9fc8190a296f4f2b6d0d5e1 |
completed | April 29, 2026, 10:43 a.m. |
| PD | Predicate disambiguation | batch_69f176585f3481909beb907de252cd98 |
completed | April 29, 2026, 3:09 a.m. |
| PDg | Predicate description generation | batch_69f1785afe3c81909be28986ffe944bf |
completed | April 29, 2026, 3:17 a.m. |
Created at: April 17, 2026, 11:31 p.m.