Triple
T10620782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Larisa |
E250192
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Lara |
E77944
|
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: Lara | Statement: [Larisa, hasDiminutive, Lara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lara Context triple: [Larisa, hasDiminutive, Lara]
-
A.
Lara
chosen
Lara is a feminine given name, often used in various cultures and languages, sometimes as a variant of Laura or derived from Latin and Russian origins.
-
B.
Lara
Lara is a semi-autobiographical novel by British writer Bernardine Evaristo that explores themes of identity, heritage, and family across generations.
-
C.
Lara Sanoica
Lara Sanoica is an American local politician who serves as the mayor of Rolling Meadows, Illinois.
-
D.
Lara Belmont
Lara Belmont is a British actress best known for her role in the war drama film "The War Zone."
-
E.
Lara, Victoria
Lara, Victoria is a regional township in the City of Greater Geelong, Australia, known as a growing commuter suburb between Melbourne and Geelong.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df70b0288190bf6edd705632ff02 |
completed | April 8, 2026, 11:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96b908e788190bc9e4f327e871a7f |
completed | April 10, 2026, 9:28 p.m. |
Created at: April 8, 2026, 8:49 p.m.