Triple
T15403720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lys River |
E368392
|
entity |
| Predicate | sourceLocation |
P40
|
FINISHED |
| Object |
Lisbourg
Lisbourg is a commune in northern France known for its rural landscape and as the birthplace of the Lys River.
|
E1155295
|
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: Lisbourg | Statement: [Lys River, sourceLocation, Lisbourg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisbourg Context triple: [Lys River, sourceLocation, Lisbourg]
-
A.
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.
-
B.
Port-Joinville
Port-Joinville is the main port town on the Île d’Yeu off the west coast of France, known as a ferry hub and seaside destination in the Vendée department.
-
C.
Rive-de-Gier
Rive-de-Gier is a commune in central France’s Loire department, historically known for its coal mining and glassmaking industries.
-
D.
Maubeuge
Maubeuge is a fortified industrial town in northern France near the Belgian border, historically significant for its strategic military position.
-
E.
Kaysersberg
Kaysersberg is a picturesque medieval town in France’s Alsace region, renowned for its half-timbered houses, hillside vineyards, and well-preserved historic charm.
- 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: Lisbourg Triple: [Lys River, sourceLocation, Lisbourg]
Generated description
Lisbourg is a commune in northern France known for its rural landscape and as the birthplace of the Lys River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lisbourg Target entity description: Lisbourg is a commune in northern France known for its rural landscape and as the birthplace of the Lys River.
-
A.
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.
-
B.
Port-Joinville
Port-Joinville is the main port town on the Île d’Yeu off the west coast of France, known as a ferry hub and seaside destination in the Vendée department.
-
C.
Rive-de-Gier
Rive-de-Gier is a commune in central France’s Loire department, historically known for its coal mining and glassmaking industries.
-
D.
Maubeuge
Maubeuge is a fortified industrial town in northern France near the Belgian border, historically significant for its strategic military position.
-
E.
Kaysersberg
Kaysersberg is a picturesque medieval town in France’s Alsace region, renowned for its half-timbered houses, hillside vineyards, and well-preserved historic charm.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8fde64819082ec0c68df305561 |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff13584f8881908b2527c51f85ae28 |
completed | May 9, 2026, 10:58 a.m. |
| NEDg | Description generation | batch_69ff145ac8e081908b075cee67e82aa3 |
completed | May 9, 2026, 11:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff1509e5a48190b69f1a44d793e07d |
completed | May 9, 2026, 11:05 a.m. |
Created at: April 10, 2026, 3:19 a.m.