Triple
T33264952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beatrice |
E851618
|
entity |
| Predicate | hasReluctantRomanceWith |
P26473
|
FINISHED |
| Object | Benedick |
E259332
|
NE 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: Benedick | Statement: [Beatrice, hasReluctantRomanceWith, Benedick]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReluctantRomanceWith Context triple: [Beatrice, hasReluctantRomanceWith, Benedick]
-
A.
hasRomanticTensionWith
chosen
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
B.
hasFictionalRomanticInterest
Indicates that one entity is portrayed as having a romantic attraction or interest toward another entity within a fictional context.
-
C.
hasRomanticEntanglementInPlot
Indicates that a romantic relationship or involvement between characters is a significant element within the narrative plot.
-
D.
hasRomanticEncounterWith
Indicates that two entities engage in or share a romantic or intimate encounter with each other.
-
E.
hasRomanticMisadventures
Indicates that an entity experiences a series of problematic, comical, or unsuccessful romantic relationships or encounters.
- F. None of above.
Provenance (4 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_69f349642dac81908a37ffcc3b976a55 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a036054129081909e66c5d62015e6c1 |
completed | May 12, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a35431412408190a2a16f174b43f6df |
completed | June 19, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_6a035f17cbf88190acd6bdbfc9789ffb |
completed | May 12, 2026, 5:10 p.m. |
Created at: May 1, 2026, 1:32 a.m.