Triple
T8090791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nell Burton |
E188854
|
entity |
| Predicate | givenName |
P17
|
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 Burton, givenName, Nell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nell Context triple: [Nell Burton, givenName, 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.
Elly
Elly is a feminine given name used in various cultures, often as a diminutive of names like Elisabeth or Eleanor.
-
E.
Nenê
Nenê is a Brazilian professional basketball player and longtime NBA center known for his physical interior play and key contributions to both the Denver Nuggets and Washington Wizards.
- 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_69ca82b7b3e88190b9041ab0ef28b3cb |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb421fb8348190b6495394d498d3f4 |
completed | March 31, 2026, 3:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc640a42648190bc1a3072eb338e22 |
completed | April 1, 2026, 12:17 a.m. |
Created at: March 30, 2026, 5:29 p.m.