Triple
T14669075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sully Erna |
E344458
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Erna |
E623911
|
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: Erna | Statement: [Sully Erna, familyName, Erna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Erna Context triple: [Sully Erna, familyName, Erna]
-
A.
Erna
chosen
Erna is the given name of Erna Schneider Hoover, an American mathematician and pioneering computer scientist known for revolutionizing telephone switching systems.
-
B.
Ema
Ema is a given name used as a variant spelling of Emma in various languages and cultures.
-
C.
Ema
Ema is an Austronesian language spoken primarily in East Timor, also known as the Kemak language.
-
D.
Maritta
Maritta is a feminine given name, typically considered a variant of names like Marita or Maria used in various European cultures.
-
E.
Magdalena
Magdalena is a historic town in the Mexican state of Jalisco, known for its role in the tequila-producing region and its proximity to agave landscapes and traditional distilleries.
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb54dda1c8190bf16d17e26a2bba6 |
completed | April 14, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5e9bb2081908515ab6430e9b1c2 |
completed | May 8, 2026, 12:24 p.m. |
Created at: April 10, 2026, 1:27 a.m.