Triple
T1800327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janet Asimov |
E39702
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Janet |
E74976
|
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: Janet | Statement: [Janet Asimov, givenName, Janet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Janet Context triple: [Janet Asimov, givenName, Janet]
-
A.
Janet
chosen
Janet is a feminine given name commonly used in English-speaking countries, often associated with notable figures in entertainment and public life.
-
B.
Janice
Janice is a feminine given name commonly used in English-speaking countries.
-
C.
Diane
Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
-
D.
Juanita
Juanita is a feminine given name of Spanish origin commonly used in English- and Spanish-speaking countries.
-
E.
Nancy
Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
- 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_69a88632aa588190ba3978fde0db5bbd |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa656ad5d4819090e677ad137b0cd1 |
completed | March 6, 2026, 5:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0ab743fc8190b181929109642e36 |
completed | March 8, 2026, 11:48 p.m. |
Created at: March 4, 2026, 7:32 p.m.