Triple
T13744356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Bishop |
E330175
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Elizabeth |
E307144
|
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: Elizabeth | Statement: [Elizabeth Bishop, givenName, Elizabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Context triple: [Elizabeth Bishop, givenName, Elizabeth]
-
A.
Elizabeth
chosen
Elizabeth is the given name of the renowned Victorian-era English poet Elizabeth Barrett Browning.
-
B.
Elizabeth
Elizabeth Cromwell was the wife of English military and political leader Oliver Cromwell and served as the de facto first lady during his tenure as Lord Protector.
-
C.
Elizabeth
Elizabeth is the given name of Elizabeth Parke Custis, the eldest granddaughter of Martha Washington and a prominent member of early American society.
-
D.
Elizabeth
Elizabeth of Aragon, also known as Saint Elizabeth of Portugal, was a 13th–14th century queen consort renowned for her piety, charity, and role as a peacemaker in dynastic conflicts.
-
E.
Elizabeth
Elizabeth was a German noblewoman who held the title of Landgravine of Hesse-Homburg.
- 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de020855ec8190a60fa1cb761f2e68 |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a8501890819081152bc2e9c06836 |
completed | May 3, 2026, 7:56 p.m. |
Created at: April 9, 2026, 10:08 p.m.