Triple
T28275004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toni Simmons |
E712962
|
entity |
| Predicate | hasRomanticInvolvementInPlotWith |
P176284
|
FINISHED |
| Object | Igor Sullivan |
—
|
NE NERFINISHED |
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: Igor Sullivan | Statement: [Toni Simmons, hasRomanticInvolvementInPlotWith, Igor Sullivan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanticInvolvementInPlotWith Context triple: [Toni Simmons, hasRomanticInvolvementInPlotWith, Igor Sullivan]
-
A.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
B.
hasRomanticEntanglementInPlot
chosen
Indicates that a romantic relationship or involvement between characters is a significant element within the narrative plot.
-
C.
isRomanticLeadOf
Indicates that one entity serves as the primary romantic partner or love-interest counterpart to another entity within a narrative or story.
-
D.
romanticPartnerInSeries
Indicates that one character is portrayed as a romantic partner of another character within the context of a specific series or narrative.
-
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_69efb52275788190ae5181ccebef18ce |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f74c70fd248190a9d5543afcb08211 |
completed | May 3, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69f7478e3b548190a51d5d436e2bb036 |
completed | May 3, 2026, 1:03 p.m. |
Created at: April 27, 2026, 11:19 p.m.