Triple
T14252161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Mays |
E353295
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Des |
E329596
|
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: Des | Statement: [Daniel Mays, notableWork, Des]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Des Context triple: [Daniel Mays, notableWork, Des]
-
A.
Des
chosen
Des is a given name, typically used as a shortened form of Desmond.
-
B.
Dies
Dies is the Roman personification and goddess of Day, corresponding to the Greek goddess Hemera.
-
C.
Dar
Dar is the warrior protagonist and titular Beastmaster of the Beastmaster fantasy franchise, known for his ability to telepathically communicate with and command animals.
-
D.
Dar
Dar is a character from the 1935 French film "Princesse Tam-Tam," which starred Josephine Baker.
-
E.
Den
Den was a prominent pharaoh of Egypt’s First Dynasty, known for early administrative innovations and military campaigns that helped consolidate the young Egyptian state.
- 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6296f9d0819086f62f525d07eb12 |
completed | April 14, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd325a14b881909522b6fbbcc6326f |
completed | May 8, 2026, 12:46 a.m. |
Created at: April 10, 2026, 1:08 a.m.