Triple
T21061957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Electra |
E518870
|
entity |
| Predicate | grandmother |
P3524
|
FINISHED |
| Object | Aerope |
—
|
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: Aerope | Statement: [Electra, grandmother, Aerope]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aerope Context triple: [Electra, grandmother, Aerope]
-
A.
Aerope
chosen
Aerope is a figure in Greek mythology, a Cretan princess best known as the wife of Atreus and mother of the Atreidae, whose tragic family saga underpins much of the Trojan War cycle.
-
B.
Aiginio
Aiginio is a town in northern Greece that serves as a local center within the Pieria regional unit, known for its agricultural surroundings and proximity to the Thermaic Gulf.
-
C.
Eura
Eura is a municipality in southwestern Finland known for its rich archaeological heritage and prehistoric sites.
-
D.
Aprica
Aprica is a popular ski resort town in the Italian Alps known for its extensive slopes and winter sports facilities.
-
E.
Avelia
Avelia is a family of high-speed trainsets developed by Alstom and used in various advanced rail networks around the world.
- 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_69e0b505ef108190b25dd4033e2ff7eb |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6feb064a48190b892b78e27e8d0fa |
completed | April 21, 2026, 4:36 a.m. |
Created at: April 16, 2026, 2:38 p.m.