Triple
T976980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little Women (1994 film) |
E21075
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Little Women (novel) |
E47453
|
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: Little Women (novel) | Statement: [Little Women (1994 film), basedOn, Little Women (novel)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Little Women (novel) Context triple: [Little Women (1994 film), basedOn, Little Women (novel)]
-
A.
Little Women
chosen
Little Women is a classic coming-of-age novel by Louisa May Alcott that follows the lives, struggles, and personal growth of the four March sisters during and after the American Civil War.
-
B.
Little Men
Little Men is a classic 19th-century novel by Louisa May Alcott that follows the lives and moral growth of a group of boys at a progressive school run by Jo March and her husband.
-
C.
Shirley
Shirley is the given name of Shirley Ann Jackson, a prominent American physicist and trailblazing academic leader.
-
D.
Shirley
Shirley is a small town in north-central Massachusetts served by commuter rail on the MBTA Fitchburg Line.
-
E.
Shirley
Shirley is an English surname of Old English origin that has also become a common given name.
- 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_69a493c2b62c8190b616351789ec47f8 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b46344048190b7a13b8f3ad9f455 |
completed | March 1, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac170e8a008190a40001224f8dae2a |
completed | March 7, 2026, 12:16 p.m. |
Created at: March 1, 2026, 7:40 p.m.