Triple
T604782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scott Brown |
E11570
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Brown |
E5209
|
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: Brown | Statement: [Scott Brown, familyName, Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brown Context triple: [Scott Brown, familyName, Brown]
-
A.
Brown Base
Brown Base is an Argentine research station in Antarctica that operates seasonally to support scientific studies in the region.
-
B.
Orange
Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
-
C.
Orange
Orange is a regional city in the Central Tablelands of New South Wales, Australia, known for its cool-climate wines, agriculture, and growing tourism industry.
-
D.
Black
Black is a nominative report series of early United States Supreme Court decisions compiled and published under the name of the court reporter Black.
-
E.
Black
chosen
Black is a common English-language surname borne by numerous notable individuals across diverse fields.
- 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_69a4932779b881908688590d59c71900 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49dc67b248190b0bb195553f03be8 |
completed | March 1, 2026, 8:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5238addd48190a1f4f471715fccea |
completed | March 2, 2026, 5:43 a.m. |
Created at: March 1, 2026, 7:35 p.m.