Triple
T10553795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beauty Shop |
E249021
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Jorge |
E585502
|
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: Jorge | Statement: [Beauty Shop, character, Jorge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jorge Context triple: [Beauty Shop, character, Jorge]
-
A.
Jorge
chosen
Jorge is a character portrayed by actor Giancarlo Esposito, known for his nuanced and often intense roles in film and television.
-
B.
Jorge
Jorge is the birth name of Pope Francis, the head of the Roman Catholic Church and the first pope from the Americas.
-
C.
Jorge
Jorge is the given name of the renowned Argentine writer and poet Jorge Luis Borges, a central figure in 20th-century literature.
-
D.
Jorge
Jorge is a masculine given name of Spanish and Portuguese origin, equivalent to George in English.
-
E.
Jorge
Jorge is a fictional character who appears in the Mexican film "Viridiana," directed by Luis Buñuel.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d527118da081909ca61bc555a17609 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95e6e28a481909a90059e6ce51f6d |
completed | April 10, 2026, 8:32 p.m. |
Created at: April 6, 2026, 12:34 p.m.