Triple
T7606497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarthe River |
E180117
|
entity |
| Predicate | flowsThroughCity |
P10456
|
FINISHED |
| Object | Alençon |
E348749
|
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: Alençon | Statement: [Sarthe River, flowsThroughCity, Alençon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alençon Context triple: [Sarthe River, flowsThroughCity, Alençon]
-
A.
Alençon
chosen
Alençon is a historic town in northwestern France renowned for its fine lace-making tradition and architectural heritage.
-
B.
Chapeauroux
Chapeauroux is a river in central France that flows through the Massif Central before joining the Allier.
-
C.
Evreux
Evreux is a historic town in northern France, known for its Gothic cathedral and role as the capital of the Eure department in Normandy.
-
D.
Doué-en-Anjou
Doué-en-Anjou is a commune in western France known for its troglodyte dwellings and wine production in the Maine-et-Loire department of the Pays de la Loire region.
-
E.
Angers
Angers is a historic city in western France known for its medieval architecture, including the Château d'Angers and its famous Apocalypse Tapestry.
- 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_69c69f3567008190ab01d2ca7b53584a |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9fe10408190b1c12bb8f911cea8 |
completed | March 27, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9d13a8b108190a0b13592f3a6362e |
completed | March 30, 2026, 1:26 a.m. |
Created at: March 27, 2026, 3:54 p.m.