Triple
T29350995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Snow |
E744311
|
entity |
| Predicate | hasRomanticPlotRole |
P177260
|
FINISHED |
| Object | love interest of Felix Graham |
—
|
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: love interest of Felix Graham | Statement: [Mary Snow, hasRomanticPlotRole, love interest of Felix Graham]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanticPlotRole Context triple: [Mary Snow, hasRomanticPlotRole, love interest of Felix Graham]
-
A.
hasRomanticPlotline
Indicates that there is a romantic storyline or relationship development present between the entities.
-
B.
isRomanticLeadOf
chosen
Indicates that one entity serves as the primary romantic partner or love-interest counterpart to another entity within a narrative or story.
-
C.
hasRomanticSubplot
Indicates that a work includes a secondary storyline centered on a romantic relationship between characters.
-
D.
hasRomanticEntanglementInPlot
Indicates that a romantic relationship or involvement between characters is a significant element within the narrative plot.
-
E.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
- 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_69f0a79a2d748190bc30abd469298b37 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f7c29e1b848190b945c6c6120a5330 |
completed | May 3, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b6e7a881908deb96bedb2713f4 |
completed | May 3, 2026, 9:44 p.m. |
Created at: April 28, 2026, 2:06 p.m.