Triple
T20133901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Lerna |
E490969
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Argolis |
—
|
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: Argolis | Statement: [Lake Lerna, locatedIn, Argolis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Argolis Context triple: [Lake Lerna, locatedIn, Argolis]
-
A.
Argolis
chosen
Argolis is a historic region in the northeastern Peloponnese of Greece, known for its rich archaeological heritage and ancient city-states.
-
B.
Griese
Griese is a surname most prominently associated with Bob Griese, the Hall of Fame American football quarterback.
-
C.
Breeland
Breeland is the surname of the fictional Lemon Breeland, a character from the television series "Hart of Dixie."
-
D.
Cloyce
Cloyce is a surname most notably associated with Sarah Cloyce, one of the women accused during the Salem witch trials in 17th-century Massachusetts.
-
E.
Orlondo
Orlondo is a less common variant of the given name Orlando, typically used as a masculine first name.
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6676556c481909ffc80cd2009b65a |
completed | April 20, 2026, 5:50 p.m. |
Created at: April 11, 2026, 11:32 p.m.