Triple
T17678947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dawn Richard |
E440716
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Dawn |
—
|
NE NERFINISHED |
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: Dawn | Statement: [Dawn Richard, givenName, Dawn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dawn Context triple: [Dawn Richard, givenName, Dawn]
-
A.
Dawn
Dawn is a novel by Elie Wiesel that explores the moral and psychological struggles of a young Holocaust survivor involved in a Jewish underground movement in British-controlled Palestine.
-
B.
Dawn
Dawn was a NASA space probe that studied the protoplanet Vesta and the dwarf planet Ceres in the asteroid belt using ion propulsion.
-
C.
Dawn
"Dawn" is a lesser-known novel by American author Eleanor H. Porter, best known for writing "Pollyanna."
-
D.
Dawn
chosen
Dawn is a leading American dishwashing liquid brand known for its strong grease-cutting power and use in wildlife rescue efforts.
-
E.
Dawn
Dawn is a central character in the crime thriller film "Catch .44," around whom much of the movie’s tense, intersecting plot revolves.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6f8054819087b2fe9bc8ad8d2f |
completed | April 19, 2026, 6 a.m. |
Created at: April 10, 2026, 10:01 a.m.