Triple
T10638854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alice Maud Mary |
E250664
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Maud |
E117170
|
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: Maud | Statement: [Alice Maud Mary, givenName, Maud]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maud Context triple: [Alice Maud Mary, givenName, Maud]
-
A.
Maud
chosen
Maud is a feminine given name of Germanic origin, historically borne by European royalty and nobility.
-
B.
Maud
Maud was a Norwegian polar exploration ship used by Roald Amundsen during his Arctic expeditions in the early 20th century.
-
C.
Maud
Maud is a small village in Aberdeenshire, Scotland, known historically as a rural railway junction and agricultural center.
-
D.
Muriel
Muriel is a feminine given name of French origin that has been borne by various notable figures, including politicians, writers, and artists.
-
E.
Sybylla
Sybylla is the spirited, independent-minded young heroine and narrator of Miles Franklin’s classic Australian novel "My Brilliant Career."
- 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfaf12188190a5774d4d64674653 |
completed | April 8, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96bcd8c0c8190a0fad6a85b5604bb |
completed | April 10, 2026, 9:29 p.m. |
Created at: April 8, 2026, 9:04 p.m.