Triple
T22910199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darla Moore |
E568567
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Darla |
—
|
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: Darla | Statement: [Darla Moore, givenName, Darla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Darla Context triple: [Darla Moore, givenName, Darla]
-
A.
Darla
chosen
Darla is a central child character from the classic "Our Gang" (also known as "The Little Rascals") comedy shorts, often portrayed as the charming object of the boys’ affections.
-
B.
Syndi
Syndi is the central protagonist of the "Mode" science fiction/fantasy novel series by Piers Anthony.
-
C.
Darlene
Darlene is an American actress best known for her role as the housebound mother in the film "What's Eating Gilbert Grape."
-
D.
Darlene
Darlene is a skilled hacker and key member of the fsociety collective in the television series "Mr. Robot."
-
E.
Darlene
Darlene is a fictional character portrayed by actress Dominique Fishback, known from her work in film and television dramas.
- 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_69e2458cd9e48190943ad2e34485d939 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1807350008190a057e8fb5c363c5f |
completed | April 29, 2026, 3:52 a.m. |
Created at: April 17, 2026, 3:42 p.m.