Triple
T5356705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nell Kellty |
E102709
|
entity |
| Predicate | appearsIn |
P795
|
FINISHED |
| Object | Nell |
E13447
|
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: Nell | Statement: [Nell Kellty, appearsIn, Nell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nell Context triple: [Nell Kellty, appearsIn, Nell]
-
A.
Nell
chosen
Nell is a feminine given name, often used as a diminutive of names like Eleanor or Helen.
-
B.
Nellie
Nellie is the familiar nickname of Nellie Connally, the former First Lady of Texas who was riding in the car with President John F. Kennedy during his assassination in 1963.
-
C.
Lila
Lila is a central female character in Max Frisch’s novel "Mein Name sei Gantenbein," around whom the narrator constructs one of his imagined lives and relationships.
-
D.
Neely
Neely is the surname of Cam Neely, a former professional ice hockey player and current executive best known for his career with the Boston Bruins.
-
E.
Nene
Nene was the principal wife of Japanese warlord Toyotomi Hideyoshi and a politically influential noblewoman during the late Sengoku period.
- 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_69bd43d8f7248190b64c140734b5c9a8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd863099b081909d20f7014b98de5a |
completed | March 20, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21e2b7b08190aca4c2855ff041de |
completed | March 21, 2026, 10:55 p.m. |
Created at: March 20, 2026, 2:01 p.m.