Triple
T15987892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theresa Lopez-Fitzgerald |
E387743
|
entity |
| Predicate | majorStoryline |
P39504
|
FINISHED |
| Object | pursuit of true love with Ethan Crane |
—
|
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: pursuit of true love with Ethan Crane | Statement: [Theresa Lopez-Fitzgerald, majorStoryline, pursuit of true love with Ethan Crane]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorStoryline Context triple: [Theresa Lopez-Fitzgerald, majorStoryline, pursuit of true love with Ethan Crane]
-
A.
storyline
Indicates that one entity serves as the narrative plot or sequence of events associated with another entity.
-
B.
majorSeries
Indicates that one entity is a primary or central installment within a larger series or sequence of related works.
-
C.
notableStoryArc
chosen
Indicates that there exists a significant or prominent narrative storyline or plot development involving the subject.
-
D.
majorEpic
Indicates that an entity is a principal or most significant epic work associated with another entity (such as an author, tradition, or corpus).
-
E.
majorPlotPoint
Indicates that an event or development plays a central, pivotal role in the overall progression or outcome of the plot.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4e871c819082d7b1c1eaf5b4fe |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d9d8e881909b559a3e3ca21d24 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.