Triple
T4023247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Compiègne Forest |
E91328
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Compiègne |
E173943
|
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: Compiègne | Statement: [Compiègne Forest, nearbyCity, Compiègne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Compiègne Context triple: [Compiègne Forest, nearbyCity, Compiègne]
-
A.
Compiegne
chosen
Compiègne is a historic city in northern France known for its royal château, forest, and role in significant events such as the signing of the 1918 Armistice.
-
B.
Creil
Creil is a commuter town in northern France’s Oise department, known as a regional rail hub connecting Paris with Picardy via major train and RER lines.
-
C.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
-
D.
Tournus
Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
-
E.
Gonesse
Gonesse is a commune in the northeastern suburbs of Paris, France, known historically as a rural town and now as part of the greater Paris metropolitan area.
- 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_69aed9618b04819081750d979d2af098 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeface7a788190a0e4e549a6816f91 |
completed | March 9, 2026, 4:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589ccd0a48190b98dbe7268df678f |
completed | March 14, 2026, 4:16 p.m. |
Created at: March 9, 2026, 3:35 p.m.