Triple
T4757429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telegony |
E105621
|
entity |
| Predicate | relatedWork |
P37
|
FINISHED |
| Object | Cypria |
E156019
|
NE FINISHED |
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: Cypria | Statement: [Telegony, relatedWork, Cypria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cypria Context triple: [Telegony, relatedWork, Cypria]
-
A.
Cypria
chosen
Cypria is a lost ancient Greek epic poem, traditionally part of the Epic Cycle, that narrated events leading up to the Trojan War.
-
B.
Leros
Leros is a Greek island in the southeastern Aegean Sea, known for its natural harbors, World War II history, and traditional villages.
-
C.
Melos
Melos is a Greek island in the Aegean Sea, renowned in art history as the discovery site of the famous ancient statue Venus de Milo.
-
D.
Amorgos
Amorgos is a Greek island in the Cyclades known for its dramatic cliffs, clear blue waters, and traditional whitewashed villages.
-
E.
Evrytania
Evrytania is a mountainous regional unit in western Central Greece known for its rugged landscapes, forests, and traditional villages.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd43f14cac819081c7c69803648211 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64ec16a0819089836e4388b555f6 |
completed | March 20, 2026, 3:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be3a72ba908190ad9b423aea2a22b8 |
completed | March 21, 2026, 6:28 a.m. |
Created at: March 20, 2026, 1:20 p.m.