Triple
T34203267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Night Train to Munich |
E877442
|
entity |
| Predicate | reusesCharactersFrom |
P178522
|
FINISHED |
| Object | The Lady Vanishes |
—
|
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: The Lady Vanishes | Statement: [Night Train to Munich, reusesCharactersFrom, The Lady Vanishes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reusesCharactersFrom Context triple: [Night Train to Munich, reusesCharactersFrom, The Lady Vanishes]
-
A.
usesCharactersAs
Indicates that one entity employs or incorporates specific characters (such as letters, symbols, or glyphs) from another entity for its representation or functioning.
-
B.
providesAdditionalCharactersFor
Indicates that one entity supplies extra or supplementary characters to be used by another entity or process.
-
C.
representsForCharacters
Indicates that one entity performs a representation or advocacy role on behalf of specific characters.
-
D.
reusedIn
Indicates that something previously used in one context or instance is used again in another context or instance.
-
E.
reusesStructureOf
Indicates that one entity adopts or incorporates the structural design or organization of another entity.
- F. None of above. chosen
Provenance (4 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_69f349aff5f0819096275315abea5344 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f710aaff588190adc6cc5b7d5424cc |
completed | May 3, 2026, 9:08 a.m. |
| PD | Predicate disambiguation | batch_69f70f3c5bfc81908585f52e196dafe5 |
completed | May 3, 2026, 9:02 a.m. |
| PDg | Predicate description generation | batch_69f70fddd43c819088dee5a448c72cbe |
completed | May 3, 2026, 9:05 a.m. |
Created at: May 1, 2026, 1:55 a.m.