Triple
T19981916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volumnia |
E493835
|
entity |
| Predicate | alliesWith |
P435
|
FINISHED |
| Object | Valeria |
—
|
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: Valeria | Statement: [Volumnia, alliesWith, Valeria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valeria Context triple: [Volumnia, alliesWith, Valeria]
-
A.
Valeria
Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
-
B.
Valeria
Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
-
C.
Valeria
chosen
Valeria was a Roman noblewoman best known as the last wife of the dictator Lucius Cornelius Sulla during the late Roman Republic.
-
D.
Valeria
Valeria is a feminine given name used in various languages, derived from the Latin family name Valerius and associated with meanings related to strength and valor.
-
E.
Valeria
Valeria is a character in the Mexican film "Amores perros," a successful model whose life is dramatically altered by a devastating car accident.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d13a8a88190bf5f4f697793f4c9 |
completed | April 20, 2026, 5:06 p.m. |
Created at: April 11, 2026, 3:28 p.m.