Triple
T13568590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | No Exit |
E324100
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Inès |
E396507
|
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: Inès | Statement: [No Exit, mainCharacter, Inès]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Inès Context triple: [No Exit, mainCharacter, Inès]
-
A.
Inés
Inés is a feminine given name, especially common in Spanish-speaking countries, derived from the name Agnes.
-
B.
Ines
chosen
Ines is a feminine given name, commonly used in various European and Latin American countries, that is a variant of the name Agnes.
-
C.
Romina
Romina is an Italian-American actress and singer best known as half of the pop duo Al Bano & Romina Power.
-
D.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
-
E.
Rosana
Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
- 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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb00e0188819094fde44f85adb69c |
completed | April 12, 2026, 2:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78add2b0c8190ade1af991744c4e0 |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 9:48 p.m.