Triple
T1688574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bettina |
E36497
|
entity |
| Predicate | hasRelatedName |
P3889
|
FINISHED |
| Object | Elisa |
E70903
|
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: Elisa | Statement: [Bettina, hasRelatedName, Elisa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elisa Context triple: [Bettina, hasRelatedName, Elisa]
-
A.
Elisa
chosen
Elisa is a feminine given name of Hebrew origin, often considered a short form of Elisabeth and used in various languages including Italian, Spanish, and French.
-
B.
Elise
Elise is a given name associated with the Austrian-Swedish physicist Lise Meitner, a pioneer in nuclear fission research.
-
C.
Silvia
Silvia is a feminine given name used in various languages, often associated with the Latin word for "forest" or "woods."
-
D.
Rosalinda
Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
-
E.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
- 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa6296655c8190835ec0d20f7460ca |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad7992792081909af4312ae8a448a2 |
completed | March 8, 2026, 1:28 p.m. |
Created at: March 4, 2026, 7:29 p.m.