Triple
T110565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Adventures of Tom Sawyer |
E2238
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Becky Thatcher |
E11794
|
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: Becky Thatcher | Statement: [The Adventures of Tom Sawyer, hasCharacter, Becky Thatcher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Becky Thatcher Context triple: [The Adventures of Tom Sawyer, hasCharacter, Becky Thatcher]
-
A.
Becky Thatcher
chosen
Becky Thatcher is a spirited, kind-hearted girl in Mark Twain’s classic novel "The Adventures of Tom Sawyer," known as Tom’s love interest and a symbol of youthful innocence and adventure.
-
B.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
C.
Nance
Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
D.
Jem
Jem is a common diminutive or nickname for the given name James.
-
E.
Angela
Angela is the given name of Angela Merkel, the long-serving former Chancellor of Germany and a prominent European political leader.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a256ce54b48190a3337f5f45d82859 |
completed | Feb. 28, 2026, 2:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a284fed06c81909df34f4227f26e7d |
completed | Feb. 28, 2026, 6:02 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.