Triple
T12870649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bridge on the River Kwai |
E307839
|
entity |
| Predicate | hasSubjectIn |
P79706
|
FINISHED |
| Object | war museums in Kanchanaburi |
—
|
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: war museums in Kanchanaburi | Statement: [Bridge on the River Kwai, hasSubjectIn, war museums in Kanchanaburi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubjectIn Context triple: [Bridge on the River Kwai, hasSubjectIn, war museums in Kanchanaburi]
-
A.
hasSubjectEntriesIn
Indicates that a subject is recorded or represented within specific entries of a collection, dataset, or catalog.
-
B.
hasSubjectPosition
Indicates that an entity occupies or is assigned to a particular subject role or position within a structure, context, or organization.
-
C.
hasSubjectPlace
chosen
Indicates that something is associated with or occurs in a particular subject-related place or location.
-
D.
hasTypicalSubject
Indicates that something is commonly or characteristically used as the subject (agent or topic) of a given relation or action.
-
E.
hasSubjectCount
Indicates that an entity is associated with a specific number of subjects.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97c7f91d08190aac2f6419d3ba992 |
completed | April 10, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69d96fa55b888190ab1612e93c41aec4 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:38 p.m.