Triple
T16382225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janet Murguía |
E397833
|
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 Murguía, givenName, Janet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Janet Context triple: [Janet Murguía, 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.
Janine
Janine is a feminine given name used in various cultures, often as a variant of Jeanine or Jeanne.
-
D.
Judy
Judy is the familiar nickname of Judy Agnew, who was the Second Lady of the United States during Spiro Agnew’s vice presidency.
-
E.
Judy
"Judy" is a multimedia artwork by American contemporary artist Tony Oursler, known for its experimental use of video projection and sculptural elements to explore psychological and technological themes.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e319dd0e0c8190812bde6a2f7d9644 |
completed | April 18, 2026, 5:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00356b00408190beab51a23011be67 |
completed | May 10, 2026, 7:36 a.m. |
Created at: April 10, 2026, 5:08 a.m.