Triple
T9078364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Nicholson |
E217546
|
entity |
| Predicate | notableWork |
P4
|
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: [William Nicholson, notableWork, Nell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nell Context triple: [William Nicholson, notableWork, 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 novel by Marilynne Robinson that continues her acclaimed Gilead series, exploring themes of grace, poverty, and belonging through the life of its enigmatic title character.
-
D.
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.
-
E.
Elly
Elly is a feminine given name used in various cultures, often as a diminutive of names like Elisabeth or Eleanor.
- 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_69ca83d6c14c8190bc056d927f00a2a2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc95c7d3688190a4c1c6a92965eae4 |
completed | April 1, 2026, 3:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cffe190c28819098ee017c75d72ad0 |
completed | April 3, 2026, 5:51 p.m. |
Created at: March 30, 2026, 7:12 p.m.