Triple
T2056048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hauts-de-Seine |
E45676
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Val-d'Oise |
E45085
|
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: Val-d'Oise | Statement: [Hauts-de-Seine, borders, Val-d'Oise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Val-d'Oise Context triple: [Hauts-de-Seine, borders, Val-d'Oise]
-
A.
Val-d'Oise
chosen
Val-d'Oise is a department in northern France that forms part of the Paris metropolitan region and includes both suburban areas and rural landscapes.
-
B.
Seine-et-Oise
Seine-et-Oise was a former department of France surrounding Paris, abolished in 1968 and divided into several new departments including Yvelines.
-
C.
Aisne
Aisne is a department in northern France known for its historic towns, World War I battlefields, and rural landscapes.
-
D.
Aube
Aube is a department in northeastern France known for its historic towns, Champagne vineyards, and rural landscapes.
-
E.
Sarthe
Sarthe is a department in western France known for its capital Le Mans and the famous 24 Hours of Le Mans endurance race.
- 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_69a8891a19508190a12ef1e192308dcb |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9a9ce548190a5a3488fafb2e79e |
completed | March 7, 2026, 5:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6ae88ad08190a0a638cad2566f2e |
completed | March 9, 2026, 6:38 a.m. |
Created at: March 4, 2026, 7:40 p.m.