Triple
T22550099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisa Randall |
E557535
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lisa |
—
|
NE NERFINISHED |
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: Lisa | Statement: [Lisa Randall, givenName, Lisa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisa Context triple: [Lisa Randall, givenName, Lisa]
-
A.
Lisa
chosen
Lisa is a feminine given name commonly used in English-speaking countries, often as a shortened form of Elizabeth or Melissa.
-
B.
Lisa
Lisa is a person known primarily for holding a position or role that was later taken over by Denise.
-
C.
Lisa
Lisa is the advanced AGA graphics chipset used in the Commodore Amiga 1200 computer, providing enhanced color and display capabilities over earlier Amiga systems.
-
D.
Lisa
Lisa is the first name of Dr. Lisa Cuddy, a central hospital administrator character on the medical drama television series "House."
-
E.
Lisa
Lisa is a character in "The L Word" known for being one of Alice Pieszecki’s unconventional love interests.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e59db848190b4272ecd2b690ffd |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f74512c8190b5369e19a4bc6325 |
completed | April 29, 2026, 1:31 a.m. |
Created at: April 16, 2026, 8:52 p.m.