Triple
T16435204
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regina Lampert |
E399164
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Regina |
E792977
|
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: Regina | Statement: [Regina Lampert, givenName, Regina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Regina Context triple: [Regina Lampert, givenName, Regina]
-
A.
Regina
Regina is a fictional character known for her role as a maid.
-
B.
Regina
Regina is the given first name of American actress Jenna Fischer, best known for her role as Pam Beesly on the U.S. version of "The Office."
-
C.
Regina
chosen
Regina is a feminine given name of Latin origin meaning "queen," used in various cultures and languages.
-
D.
Regina
Regina is a 1949 opera by American composer Marc Blitzstein, adapted from Lillian Hellman’s play "The Little Foxes."
-
E.
Regina, Saskatchewan, Canada
Regina, Saskatchewan, Canada is the capital city of the province of Saskatchewan, known as a major cultural and economic center on the Canadian Prairies.
- 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_69d87f2c6778819080fcfae53be8f12a |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32ba1023481909588aa6a3c677886 |
completed | April 18, 2026, 6:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0045872a688190bcab27c6a2b952cd |
completed | May 10, 2026, 8:44 a.m. |
Created at: April 10, 2026, 5:10 a.m.