Triple
T7437828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Annabel Andrews |
E171663
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Annabel |
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: Annabel | Statement: [Annabel Andrews, givenName, Annabel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annabel Context triple: [Annabel Andrews, givenName, Annabel]
-
A.
Maud
Maud was a Norwegian polar exploration ship used by Roald Amundsen during his Arctic expeditions in the early 20th century.
-
B.
Maud
Maud is a small village in Aberdeenshire, Scotland, known historically as a rural railway junction and agricultural center.
-
C.
Maud
chosen
Maud is a feminine given name of Germanic origin, historically borne by European royalty and nobility.
-
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.
Annabella
Annabella was a French film actress of the 1930s and 1940s, known for her work in both European and Hollywood cinema.
- 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_69c68a64228c8190affaec2a8127ce7b |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f34aa3388190ac300cf934042d78 |
completed | March 27, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8278cd9cc8190b88767c1432b3007 |
completed | March 28, 2026, 7:10 p.m. |
Created at: March 27, 2026, 3:13 p.m.