Triple
T8504213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisa Gardner |
E201293
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Lisa Gardner |
E201293
|
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: Lisa Gardner | Statement: [Lisa Gardner, name, Lisa Gardner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisa Gardner Context triple: [Lisa Gardner, name, Lisa Gardner]
-
A.
Lisa Gardner
chosen
Lisa Gardner is an American author best known for her bestselling crime and psychological thriller novels, including the Detective D.D. Warren and FBI Profiler series.
-
B.
Ellen Casey
Ellen Casey is the daughter of the late Pennsylvania governor and U.S. senator Robert P. Casey and a member of the prominent Casey political family.
-
C.
Laura Lippman
Laura Lippman is an American author best known for her award-winning crime and mystery novels, particularly the Tess Monaghan series set in Baltimore.
-
D.
Mary Higgins Clark
Mary Higgins Clark was a bestselling American author renowned for her suspenseful mystery and thriller novels, often featuring strong female protagonists.
-
E.
Sandra Grant
Sandra Grant is an American actress best known for her long-term marriage to legendary singer Tony Bennett.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe59d67d081908155a43b9b463fe3 |
completed | March 31, 2026, 3:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce88e0e08c819085d29157349a6ef6 |
completed | April 2, 2026, 3:18 p.m. |
Created at: March 30, 2026, 6:14 p.m.