Triple
T11019792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martine |
E260455
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Martina |
E599791
|
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: Martina | Statement: [Martine, hasVariant, Martina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martina Context triple: [Martine, hasVariant, Martina]
-
A.
Martina
Martina was a Byzantine empress and the second wife of Emperor Heraclius, known for her controversial influence at court and her role in the empire’s turbulent 7th-century politics.
-
B.
Martina
chosen
Martina is a feminine given name of Latin origin, commonly used in many European and Spanish-speaking countries.
-
C.
Renata
Renata is a young Venetian woman who becomes the poignant love interest of an aging American colonel in Ernest Hemingway’s novel "Across the River and Into the Trees."
-
D.
Renata
Renata is a vampire in the Twilight series who serves the Volturi as a powerful bodyguard with a psychic ability to repel physical attacks.
-
E.
Daniela
Daniela is a feminine given name commonly used in many languages, often as the female form of Daniel.
- 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_69d6aa9687448190b28d353b1b6a610e |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797baad408190a53fd6941a750f68 |
completed | April 9, 2026, 12:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e374e550508190aa3779191196f329 |
completed | April 18, 2026, 12:11 p.m. |
Created at: April 8, 2026, 9:25 p.m.