Triple
T20381657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dainty June |
E497846
|
entity |
| Predicate | hasMother |
P1909
|
FINISHED |
| Object | Rose |
—
|
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: Rose | Statement: [Dainty June, hasMother, Rose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rose Context triple: [Dainty June, hasMother, Rose]
-
A.
Rose
Rose is a central character in Tommy Wiseau’s cult film "The Room," known for her manipulative relationship with the protagonist, Johnny.
-
B.
Rose
Rose is a person involved in an unconventional romantic relationship with Bob Gossage.
-
C.
Rose
Rose is a fictional main character, likely a central figure in the story "Bed of Rose’s," around whom the narrative primarily revolves.
-
D.
Rose
Rose is a common English surname shared by many individuals, including the American basketball player Derrick Rose.
-
E.
Rose
Rose is a feminine given name commonly associated with the flower and often used in English-speaking countries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69e0b4a5b7908190a972e4e7e698ae94 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e678b0ee708190bdbe4aab28a61525 |
completed | April 20, 2026, 7:04 p.m. |
Created at: April 16, 2026, 11:27 a.m.