Triple
T18879178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beastmaster 2: Through the Portal of Time |
E461773
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Dar |
—
|
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: Dar | Statement: [Beastmaster 2: Through the Portal of Time, mainCharacter, Dar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dar Context triple: [Beastmaster 2: Through the Portal of Time, mainCharacter, Dar]
-
A.
Dar
chosen
Dar is the warrior protagonist and titular Beastmaster of the Beastmaster fantasy franchise, known for his ability to telepathically communicate with and command animals.
-
B.
Dar
Dar is a character from the 1935 French film "Princesse Tam-Tam," which starred Josephine Baker.
-
C.
Dal
Dal is the commonly used short form for Dalhousie University, a major public research university in Halifax, Nova Scotia, Canada.
-
D.
Dal
Dal is a village and railway station in Eidsvoll municipality in Norway, serving as a terminus for some Oslo commuter rail services.
-
E.
Der
Der was an ancient Mesopotamian city known as an important religious center associated with the worship of the god Anu.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c3d06ef481908bba297d7a1fd011 |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 10, 2026, 11:57 a.m.