Triple
T34241683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mannen på balkongen (1993 film) |
E878483
|
entity |
| Predicate | leadDetectiveCharacter |
P63938
|
FINISHED |
| Object | Martin Beck |
—
|
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: Martin Beck | Statement: [Mannen på balkongen (1993 film), leadDetectiveCharacter, Martin Beck]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadDetectiveCharacter Context triple: [Mannen på balkongen (1993 film), leadDetectiveCharacter, Martin Beck]
-
A.
hasFictionalDetective
chosen
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
-
B.
portrayedDetective
Indicates that one entity has played or depicted a detective character in a performance or work.
-
C.
hasClericalDetective
Indicates that an entity includes or is associated with a detective who is also a member of the clergy.
-
D.
detectiveType
Indicates that one entity is classified as a particular type or category of detective in relation to another entity.
-
E.
roleInMurderOnTheOrientExpress
Indicates the specific involvement or function an entity has within the context of the murder case in "Murder on the Orient Express."
- 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_69f349b22d8c819096b22df268382aa9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71362f1448190985a80ce7af475cb |
completed | May 3, 2026, 9:20 a.m. |
| PD | Predicate disambiguation | batch_69f7127884388190884f23d181a65d19 |
completed | May 3, 2026, 9:16 a.m. |
Created at: May 1, 2026, 1:56 a.m.