Triple
T24990295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Who shot J.R.? |
E625427
|
entity |
| Predicate | resolvedInEpisode |
P53537
|
FINISHED |
| Object | Who Done It |
—
|
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: Who Done It | Statement: [Who shot J.R.?, resolvedInEpisode, Who Done It]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: resolvedInEpisode Context triple: [Who shot J.R.?, resolvedInEpisode, Who Done It]
-
A.
resolvedIn
Indicates that an issue, conflict, or process is brought to a conclusion or solution within a specified context, medium, or timeframe.
-
B.
resolutionInStory
Indicates the part of a narrative where the central conflicts are resolved and the story’s outcomes are finalized.
-
C.
resolutionOf
Indicates that one entity is the formal decision, outcome, or solution produced in response to, or for the purpose of addressing, another entity such as an issue, proposal, or problem.
-
D.
associatedEpisode
chosen
Indicates that one entity is linked or connected to a particular episode as its related or relevant installment.
-
E.
appearedInEpisodeOf
Indicates that one entity made an appearance in a specific episode belonging to a television or radio series associated with the other 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_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.