Triple
T24990294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Who shot J.R.? |
E625427
|
entity |
| Predicate | episodeCliffhangerFor |
P53537
|
FINISHED |
| Object | A House Divided |
—
|
NE NERFINISHED |
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: A House Divided | Statement: [Who shot J.R.?, episodeCliffhangerFor, A House Divided]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: episodeCliffhangerFor Context triple: [Who shot J.R.?, episodeCliffhangerFor, A House Divided]
-
A.
isCliffhangerFor
Indicates that one event, scene, or narrative element ends in suspense and serves as a cliffhanger leading into another.
-
B.
containsCliffhanger
Indicates that an event, scene, or narrative segment ends in a suspenseful, unresolved way that leaves the outcome uncertain.
-
C.
hasEpisode
Indicates that something, typically a series or program, includes a specific episode as one of its constituent parts.
-
D.
associatedEpisode
chosen
Indicates that one entity is linked or connected to a particular episode as its related or relevant installment.
-
E.
episodeOfDeath
Indicates the specific event or episode during which an entity’s death occurred.
- 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_69e2ff2611c081908710457fbe6d376b |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f44a4416e881909c58f3b4d9e4b42d |
completed | May 1, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69f442c0c2e88190acd7f170f10ccef6 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 6:03 a.m.