Triple
T23239306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kate O'Brien |
E581391
|
entity |
| Predicate | hasRomanticDynamic |
P26473
|
FINISHED |
| Object | will-they-won't-they relationship with Drew Carey |
—
|
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: will-they-won't-they relationship with Drew Carey | Statement: [Kate O'Brien, hasRomanticDynamic, will-they-won't-they relationship with Drew Carey]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanticDynamic Context triple: [Kate O'Brien, hasRomanticDynamic, will-they-won't-they relationship with Drew Carey]
-
A.
hasRomanticSubplot
Indicates that a work includes a secondary storyline centered on a romantic relationship between characters.
-
B.
hasRomanticTensionWith
chosen
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
C.
includesRomanticCues
Indicates that the referenced content, behavior, or interaction contains elements suggestive of romantic interest, affection, or attraction between entities.
-
D.
romanticFeeling
Indicates that one entity experiences romantic attraction or affection toward another entity.
-
E.
romanticOutcome
Indicates that a romantic relationship or interaction between entities results in a particular outcome, such as success, failure, or change in status.
- 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_69e2460556f88190be1744a84a84173f |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f192ebaef4819083a7805537ad993f |
completed | April 29, 2026, 5:11 a.m. |
| PD | Predicate disambiguation | batch_69effcdadec0819092ec1749ee453b4e |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:10 p.m.