Triple
T23295887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Does He Love You |
E590167
|
entity |
| Predicate | hasLoveTriangleParticipants |
P84449
|
FINISHED |
| Object | husband |
—
|
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: husband | Statement: [Does He Love You, hasLoveTriangleParticipants, husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLoveTriangleParticipants Context triple: [Does He Love You, hasLoveTriangleParticipants, husband]
-
A.
romanticTriangleInvolves
chosen
Indicates a romantic relationship structure in which three individuals are mutually or asymmetrically involved in overlapping romantic connections.
-
B.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
C.
hasJealousHusbandCharacter
Indicates that an entity includes or involves a husband character who experiences or expresses jealousy in the context of the relationship or narrative.
-
D.
hasMarriagePlot
Indicates that the work’s narrative centrally involves courtship, romantic relationships, or the progression toward marriage as a key plot element.
-
E.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
- 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_69e25d1af9d88190a0b9b5e8fa608618 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f196cec9e88190b83cfd53a6455e0f |
completed | April 29, 2026, 5:27 a.m. |
| PD | Predicate disambiguation | batch_69effcf325f88190b320268c3c551abb |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 5:03 p.m.