Triple
T8084633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monkey Grip |
E188699
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Nora |
E49090
|
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: Nora | Statement: [Monkey Grip, hasMainCharacter, Nora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nora Context triple: [Monkey Grip, hasMainCharacter, Nora]
-
A.
Nora
chosen
Nora is a feminine given name of Latin origin, often used independently or as a diminutive of names like Honora, Eleanor, or Leonora.
-
B.
Nina
Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
-
C.
Nina
Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
-
D.
Nora Montgomery
Nora Montgomery is a tragic, ghostly character from the television series "American Horror Story: Murder House," known for her role as a grief-stricken 1920s socialite and wife of mad surgeon Charles Montgomery.
-
E.
Nora Hayden
Nora Hayden was an American actress and model active in the mid-20th century, known for her roles in film and television.
- 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_69ca82b662e88190b9323daab8c28a21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb415f73808190b69db386b447062e |
completed | March 31, 2026, 3:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc6402e41c819095442775938d4282 |
completed | April 1, 2026, 12:17 a.m. |
Created at: March 30, 2026, 5:29 p.m.