Triple
T24773229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Margaret Scott |
E619785
|
entity |
| Predicate | hasRomanticPlotSignificance |
P141954
|
FINISHED |
| Object | central |
—
|
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: central | Statement: [Lady Margaret Scott, hasRomanticPlotSignificance, central]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanticPlotSignificance Context triple: [Lady Margaret Scott, hasRomanticPlotSignificance, central]
-
A.
hasRomanticPlotline
Indicates that there is a romantic storyline or relationship development present between the entities.
-
B.
hasRomanticSubplot
Indicates that a work includes a secondary storyline centered on a romantic relationship between characters.
-
C.
romanticSubplotCentral
chosen
Indicates that a romantic subplot is a primary, driving element of the narrative rather than a minor or peripheral thread.
-
D.
hasRomanticSceneAt
Indicates that a romantic scene occurs at a specific location or point in time within a work or context.
-
E.
includesRomanticCues
Indicates that the referenced content, behavior, or interaction contains elements suggestive of romantic interest, affection, or attraction between entities.
- 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_69e2fabd04488190a2d13c97be745a2d |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d6ef33081908f5d36ba1ae5f473 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 4:32 a.m.