Triple
T4934887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peugeot 2008 |
E110787
|
entity |
| Predicate | assemblyLocation |
P40
|
FINISHED |
| Object | Mulhouse, France |
E78039
|
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: Mulhouse, France | Statement: [Peugeot 2008, assemblyLocation, Mulhouse, France]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mulhouse, France Context triple: [Peugeot 2008, assemblyLocation, Mulhouse, France]
-
A.
Mulhouse
chosen
Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
-
B.
Ribemont, France
Ribemont, France is a small commune in northern France best known as the birthplace of Enlightenment philosopher and mathematician Marquis de Condorcet.
-
C.
Sochaux, France
Sochaux, France is an industrial town in eastern France best known as the historic home of the Peugeot automobile manufacturing complex.
-
D.
Auxerre, France
Auxerre, France is a historic city in the Burgundy region known for its medieval architecture, Gothic cathedral, and role as a cultural and economic center along the Yonne River.
-
E.
Montreuil, France
Montreuil is a suburban commune in the eastern part of the Paris metropolitan area, known for its diverse population and mix of residential, commercial, and cultural spaces.
- 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7066ed548190a76a9559f90e3869 |
completed | March 20, 2026, 4:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be81c5f8ec8190834c624bae17adff |
completed | March 21, 2026, 11:32 a.m. |
Created at: March 20, 2026, 1:30 p.m.