Triple
T7005360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rogier Stoffers |
E162439
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | The 33 |
E502217
|
NE 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: The 33 | Statement: [Rogier Stoffers, notableWork, The 33]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The 33 Context triple: [Rogier Stoffers, notableWork, The 33]
-
A.
The 33
chosen
The 33 is a 2015 drama film that recounts the true story of the 2010 Chilean mining disaster and the rescue of 33 trapped miners.
-
B.
The 305
The 305 is a nickname commonly used to refer to Miami, Florida, derived from its original area code.
-
C.
13 Going on 30
13 Going on 30 is a 2004 romantic comedy fantasy film about a 13-year-old girl who magically wakes up in her 30-year-old body and must navigate adulthood, starring Jennifer Garner.
-
D.
Time for Three
Time for Three is a genre-blending string trio known for fusing classical music with jazz, pop, and other contemporary styles in highly energetic performances.
-
E.
In 3-D
In 3-D is "Weird Al" Yankovic's 1984 comedy album that helped launch him to mainstream fame with parody hits like "Eat It."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c6885928148190ae31909fbb5e9849 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc34b5a88190a793e07dd4d0018b |
completed | March 27, 2026, 7:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a3b2e9c8190b90c4eaaaea983ee |
completed | March 28, 2026, 5:42 a.m. |
Created at: March 27, 2026, 2:33 p.m.