Triple
T23279040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Dalva |
E588806
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Dalva |
—
|
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: Dalva | Statement: [Robert Dalva, familyName, Dalva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dalva Context triple: [Robert Dalva, familyName, Dalva]
-
A.
Dalva
chosen
Dalva is a surname most notably associated with American film editor Robert Dalva, recognized for his work on major Hollywood productions.
-
B.
Dalva
Dalva is a 1988 novel by American author Jim Harrison that follows a middle-aged woman’s journey through memory, loss, and family history on the Great Plains.
-
C.
Valka
Valka is a compassionate and fiercely independent dragon rider who serves as Hiccup’s long-lost mother and a key protector of dragons in the How to Train Your Dragon film series.
-
D.
Velda
Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
-
E.
Byaelva
Byaelva is a river located in the Innherad district of Trøndelag county in central Norway.
- 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_69e25d16e2c08190a291de254703129e |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f196419eac819081d0beb5767046dc |
completed | April 29, 2026, 5:25 a.m. |
Created at: April 17, 2026, 4:49 p.m.