Triple
T23236663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ira Levinson |
E581316
|
entity |
| Predicate | storyTimeline |
P68464
|
FINISHED |
| Object | Past timeline in The Longest Ride |
—
|
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: Past timeline in The Longest Ride | Statement: [Ira Levinson, storyTimeline, Past timeline in The Longest Ride]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: storyTimeline Context triple: [Ira Levinson, storyTimeline, Past timeline in The Longest Ride]
-
A.
timelineUse
Indicates that one entity utilizes or incorporates another entity within a temporal sequence or schedule (a timeline).
-
B.
timelineDetail
Indicates a detailed view or breakdown of events, actions, or states along a timeline associated with an entity or process.
-
C.
chronologyOf
chosen
Indicates that one entity represents the temporal ordering, sequence, or historical timeline of events or states associated with another entity.
-
D.
storyline
Indicates that one entity serves as the narrative plot or sequence of events associated with another entity.
-
E.
storyBy
Indicates that one entity is the creator or author of the story associated with another entity.
- 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_69f192e98dec8190a23385600bed9ae0 |
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, 4:09 p.m.