Triple
T21326823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martha and Spencer Love School of Business |
E525779
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Martha Love |
—
|
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: Martha Love | Statement: [Martha and Spencer Love School of Business, namedAfter, Martha Love]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martha Love Context triple: [Martha and Spencer Love School of Business, namedAfter, Martha Love]
-
A.
Martha Love
chosen
Martha Love was a benefactor whose support and legacy are honored through the naming of the Martha and Spencer Love School of Business.
-
B.
Martha Wilson
Martha Wilson is a kindly but often exasperated elderly neighbor character in the Dennis the Menace franchise, frequently involved in the young troublemaker’s misadventures.
-
C.
Martha Dix
Martha Dix was the wife and frequent model of German painter Otto Dix, known from many of his portraits and family scenes.
-
D.
Martha Tilson
Martha Tilson is a vocalist best known for her work with the influential English post-punk band A Certain Ratio.
-
E.
Martha McKay
Martha McKay is a fictional character in the 2015 romantic action-comedy film "Mr. Right."
- 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_69e0b51b90788190a4dd823d962626da |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7ab4a796081908148ec9106362d3d |
completed | April 21, 2026, 4:52 p.m. |
Created at: April 16, 2026, 4:41 p.m.