Triple
T5411313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zeus |
E121017
|
entity |
| Predicate | consort |
P13
|
FINISHED |
| Object | Leto |
E19957
|
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: Leto | Statement: [Zeus, consort, Leto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leto Context triple: [Zeus, consort, Leto]
-
A.
Leto
chosen
Leto is a Titaness in Greek mythology best known as the mother of the twin Olympian deities Apollo and Artemis.
-
B.
Letňany
Letňany is a district in the northeastern part of Prague, Czech Republic, known for its residential areas, shopping centers, and transport links including a terminus of the city’s metro system.
-
C.
Automne
Automne is a river in northern France that serves as a tributary of the Oise.
-
D.
El Verano
El Verano is a small community in California’s Sonoma Valley, known for its residential character and proximity to the region’s wineries and historic towns.
-
E.
Winters
Winters is a small agricultural and wine-producing city in Northern California known for its historic downtown and proximity to outdoor recreation areas.
- 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_69bd463a41cc8190b32ff5af2b96ca93 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87985ee0819092a9a5cd6a948138 |
completed | March 20, 2026, 5:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf411f8f3c81908ca8578a388261c6 |
completed | March 22, 2026, 1:08 a.m. |
Created at: March 20, 2026, 2:05 p.m.