Triple
T15778574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A30 motorway |
E382550
|
entity |
| Predicate | servesCity |
P82
|
FINISHED |
| Object | Thionville |
E58199
|
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: Thionville | Statement: [A30 motorway, servesCity, Thionville]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thionville Context triple: [A30 motorway, servesCity, Thionville]
-
A.
Thionville
chosen
Thionville is a town in northeastern France near the Luxembourg border, known historically as a strategic industrial and military center in the Moselle region.
-
B.
Bar-le-Duc
Bar-le-Duc is a historic town in northeastern France, known as the former capital of the Duchy of Bar and for its Renaissance architecture and traditional mirabelle plum jam.
-
C.
Bois-le-Duc
Bois-le-Duc is the French name for ’s-Hertogenbosch, a historic Dutch city known for its medieval architecture and cultural heritage in the southern Netherlands.
-
D.
Pont-à-Mousson
Pont-à-Mousson is a historic town in northeastern France on the Moselle River, known for its medieval heritage and former university.
-
E.
Sarreguemines
Sarreguemines is a town in northeastern France near the German border, historically known for its ceramics and faience production.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e053fea90081908e3fe4f91475bead |
completed | April 16, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe66d78c81908308fc16c8d4e19c |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:47 a.m.