Triple
T5182900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skimbleshanks |
E116961
|
entity |
| Predicate | responsibilityInFiction |
P48208
|
FINISHED |
| Object | keeping the train in order |
—
|
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: keeping the train in order | Statement: [Skimbleshanks, responsibilityInFiction, keeping the train in order]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: responsibilityInFiction Context triple: [Skimbleshanks, responsibilityInFiction, keeping the train in order]
-
A.
fictionalUniverseRole
chosen
Indicates the role or function an entity has within a particular fictional universe or narrative setting.
-
B.
associatedWithCaseInFiction
Indicates that an entity is connected to, involved in, or relevant to a particular case or investigation within a fictional context.
-
C.
fictionalOrigin
Indicates that one entity originates from, or was first introduced within, a fictional work, universe, or narrative created by another entity.
-
D.
literaryRole
Indicates the specific narrative or functional role an entity holds within a literary work or text.
-
E.
hasReputationInFiction
Indicates that an entity is known or regarded in a particular way within fictional works or narratives.
- 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_69bd446140f08190becb93c61158f27f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd799d50388190bf2b7dfdd90949e9 |
completed | March 20, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69bd77b7e8b4819092ec3965e11f2dea |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:46 p.m.