Triple
T20818547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sir Didymus |
E512508
|
entity |
| Predicate | hasTitle |
P38
|
FINISHED |
| Object | Sir |
—
|
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: Sir | Statement: [Sir Didymus, hasTitle, Sir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sir Context triple: [Sir Didymus, hasTitle, Sir]
-
A.
Sir
"Sir" is a 1993 Hindi-language drama film directed by Mahesh Bhatt, known for its emotional story about a principled college professor and featuring a acclaimed performance by Paresh Rawal.
-
B.
Sir
chosen
Sir is a formal English honorific title traditionally used to address or refer to a knight or baronet.
-
C.
SIR
SIR is the IATA airport code for Sion Airport, a regional airport serving the town of Sion in the Swiss canton of Valais.
-
D.
SIR
SIR is a professional medical society representing physicians who specialize in minimally invasive, image-guided interventional radiology procedures.
-
E.
Mr. Sir
Mr. Sir is the gruff, intimidating counselor at Camp Green Lake in Louis Sachar’s novel "Holes," known for his harsh treatment of the boys and his distinctive sunflower seed habit.
- 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_69e0b4cd25088190b48ca9700cd24efc |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2f5b1e08190a9c26f76bd544b68 |
completed | April 21, 2026, 12:21 a.m. |
Created at: April 16, 2026, 12:41 p.m.