Triple
T2089528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crowborough |
E32634
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object |
Montargis
Montargis is a historic market town in north-central France, known for its canals, medieval architecture, and traditional praline confectionery.
|
E297593
|
NE FINISHED |
How this triple was built (4 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: Montargis | Statement: [Crowborough, hasTwinTown, Montargis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montargis Context triple: [Crowborough, hasTwinTown, Montargis]
-
A.
Tournus
Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
-
B.
Maubeuge
Maubeuge is a fortified industrial town in northern France near the Belgian border, historically significant for its strategic military position.
-
C.
Auxerre
Auxerre is a historic city in the Burgundy region of central France, known for its medieval architecture, Gothic cathedral, and role as a regional cultural and economic center.
-
D.
Troyes
Troyes is a historic city in northeastern France, known for its well-preserved medieval old town, half-timbered houses, and Gothic churches.
-
E.
Fougères
Fougères is a historic town in Brittany, northwestern France, known for its impressive medieval castle and well-preserved old quarter.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Montargis Triple: [Crowborough, hasTwinTown, Montargis]
Generated description
Montargis is a historic market town in north-central France, known for its canals, medieval architecture, and traditional praline confectionery.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Montargis Target entity description: Montargis is a historic market town in north-central France, known for its canals, medieval architecture, and traditional praline confectionery.
-
A.
Tournus
Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
-
B.
Maubeuge
Maubeuge is a fortified industrial town in northern France near the Belgian border, historically significant for its strategic military position.
-
C.
Auxerre
Auxerre is a historic city in the Burgundy region of central France, known for its medieval architecture, Gothic cathedral, and role as a regional cultural and economic center.
-
D.
Troyes
Troyes is a historic city in northeastern France, known for its well-preserved medieval old town, half-timbered houses, and Gothic churches.
-
E.
Fougères
Fougères is a historic town in Brittany, northwestern France, known for its impressive medieval castle and well-preserved old quarter.
- F. None of above. chosen
Provenance (5 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_69a885eba0708190999696a45cbec816 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba730a5c8190a85be72149574d79 |
completed | March 7, 2026, 5:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc01679b08190888249a147330288 |
completed | March 10, 2026, 6:54 a.m. |
| NEDg | Description generation | batch_69afc1bc172c8190a92cc6f2af1fdf9b |
completed | March 10, 2026, 7:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc20a59388190909dc3233581bd74 |
completed | March 10, 2026, 7:02 a.m. |
Created at: March 4, 2026, 7:43 p.m.