Triple
T32937497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chloe |
E842570
|
entity |
| Predicate | hasInfidelityPlot |
P115233
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Chloe, hasInfidelityPlot, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInfidelityPlot Context triple: [Chloe, hasInfidelityPlot, true]
-
A.
hasMaritalInfidelitySubplot
chosen
Indicates that the work includes a subplot involving a character engaging in romantic or sexual infidelity within a marriage.
-
B.
hasIllegitimateChildPlotline
Indicates that a narrative includes a storyline involving a character having a child born outside of a legally or socially recognized partnership.
-
C.
hasAffairWith
Indicates that one entity is engaged in a secret or illicit romantic or sexual relationship with another entity, typically outside a committed partnership.
-
D.
wasCheatedOnBy
Indicates that one entity was the victim of infidelity committed by another entity in a romantic or committed relationship.
-
E.
hasRomanticEntanglementInPlot
Indicates that a romantic relationship or involvement between characters is a significant element within the narrative plot.
- 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_69f34949727c81909d195c97de3341c8 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ffc1550cb481908628e446d9b67f7b |
completed | May 9, 2026, 11:20 p.m. |
| PD | Predicate disambiguation | batch_69ffc10a74708190ae90e2c378791f70 |
completed | May 9, 2026, 11:19 p.m. |
Created at: May 1, 2026, 1:20 a.m.