Triple
T11214033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laon |
E265383
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Saint-Quentin |
E214011
|
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: Saint-Quentin | Statement: [Laon, hasNearbyCity, Saint-Quentin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saint-Quentin Context triple: [Laon, hasNearbyCity, Saint-Quentin]
-
A.
Saint-Quentin
chosen
Saint-Quentin is a historic town in northern France known for its Gothic basilica, Art Deco architecture, and role as a regional administrative and commercial center.
-
B.
Mézières
Mézières is a French town historically known as a military and engineering education center, notably associated with the prestigious École royale du génie.
-
C.
Soissons
Soissons is a historic town in northern France known for its strategic military importance and notable battles throughout European history.
-
D.
Château-Thierry
Château-Thierry is a historic town in northern France known for its World War I battlefields and its association with the poet Jean de La Fontaine.
-
E.
Péronne
Péronne is a historic town in northern France known for its role in World War I and its location in the Somme department.
- 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8d7f47c8190b78c640ff1a01943 |
completed | April 9, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e55630478c8190aeee4cc219209ccb |
completed | April 19, 2026, 10:24 p.m. |
Created at: April 8, 2026, 9:30 p.m.