Triple
T6871035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Mure |
E158545
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Mure |
E430882
|
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: Mure | Statement: [Elizabeth Mure, familyName, Mure]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mure Context triple: [Elizabeth Mure, familyName, Mure]
-
A.
Mure
chosen
Mure is a Scottish surname historically associated with Lowland families and often considered a variant of or related to the name Muir.
-
B.
Moura
Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
-
C.
Moura
Moura is a small coal-mining town in Central Queensland, Australia, known for its agricultural activities and history of mining disasters.
-
D.
Nahe
Nahe is a renowned German wine region, particularly celebrated for producing high-quality Riesling wines with diverse styles due to its varied soils and microclimates.
-
E.
Yerre
The Yerre is a river in northern France that flows through the Île-de-France region before joining the Loir.
- 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_69c68831e3648190a643c328122e4d43 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8aa47f48190bc7cad3cc652f530 |
completed | March 27, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c742a841548190abd706ea1efd622f |
completed | March 28, 2026, 2:53 a.m. |
Created at: March 27, 2026, 2:22 p.m.