Triple
T20470738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darrell K Royal |
E502185
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Royal |
—
|
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: Royal | Statement: [Darrell K Royal, familyName, Royal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Royal Context triple: [Darrell K Royal, familyName, Royal]
-
A.
Royal
chosen
Royal is a French surname most prominently associated with politician Ségolène Royal, a leading figure in contemporary French public life.
-
B.
Royal
Royal is the given name of American character actor Royal Dano, known for his distinctive voice and roles in Westerns and classic films.
-
C.
Regal
Regal is a character featured in the puzzle-adventure video game "Room 25."
-
D.
Regal
Regal is a major American movie theater chain known for operating multiplex cinemas across the United States.
-
E.
Royal Highness
"Royal Highness" is a formal style used to address or refer to certain members of a royal family, typically princes and princesses, signifying high but not sovereign rank.
- 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_69e0b4ae5f1081908768b0c9a3a0bf38 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e699608d7c8190910217817e789915 |
completed | April 20, 2026, 9:23 p.m. |
Created at: April 16, 2026, 11:33 a.m.