Triple
T22058833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Carl Allison |
E545096
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Allison |
—
|
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: Allison | Statement: [David Carl Allison, familyName, Allison]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allison Context triple: [David Carl Allison, familyName, Allison]
-
A.
Allison
chosen
Allison is a common English-language surname of Scottish and Irish origin, often derived from "son of Alice" or "son of Alan."
-
B.
Allison
Allison is a key character in the film "Hocus Pocus," known as Max Dennison’s love interest and ally in battling the Sanderson sisters.
-
C.
Allison
Allison is a free-spirited, eccentric love interest played by Zooey Deschanel in the comedy film "Yes Man."
-
D.
Allison Blake
Allison Blake is a high-ranking government liaison and later head of Global Dynamics in the science-fiction TV series "Eureka," known for balancing bureaucratic oversight with genuine care for the town’s eccentric geniuses.
-
E.
Alison
Alison is a central character in Alan Garner's supernatural novel "The Owl Service," whose experiences drive the story's exploration of Welsh myth and identity.
- 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_69e11e3377c48190890c17407b9527d6 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1285ac9608190ab4f89d4ee7350d0 |
completed | April 28, 2026, 9:36 p.m. |
Created at: April 16, 2026, 8:27 p.m.