Triple
T36178867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roland Burton |
E1046650
|
entity |
| Predicate | hasStorylineAbout |
P121234
|
FINISHED |
| Object | Infidelity and marital reconciliation |
—
|
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: Infidelity and marital reconciliation | Statement: [Roland Burton, hasStorylineAbout, Infidelity and marital reconciliation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStorylineAbout Context triple: [Roland Burton, hasStorylineAbout, Infidelity and marital reconciliation]
-
A.
hasNarrativeArcOf
Indicates that one entity embodies, represents, or corresponds to the narrative arc (story progression or plot structure) of another entity.
-
B.
hasNarrativeArcIn
Indicates that an entity’s storyline or narrative progression occurs within or is contained by a specified work, context, or medium.
-
C.
hasStorylineType
chosen
Indicates that an entity’s storyline belongs to or is categorized under a specific type or narrative classification.
-
D.
hasStorylineStable
Indicates that the storyline associated with an entity remains consistent and does not undergo significant changes over time.
-
E.
hasStorylineBuild
Indicates that one element contributes to or defines the development or structure of another element’s storyline.
- 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_69f76e3c1b10819081fc7a807a71cf84 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a0031ed5d708190ab93516ad081ada6 |
completed | May 10, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_6a0031b5dbd081908eb0f4dbb8a0eac3 |
completed | May 10, 2026, 7:20 a.m. |
Created at: May 3, 2026, 4:08 p.m.