Triple
T14356391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiki van Eijk |
E355979
|
entity |
| Predicate | hasWorkedFor |
P11675
|
FINISHED |
| Object |
Saint-Louis
Saint-Louis is a historic French crystal manufacturer renowned for its high-end glassware, lighting, and decorative objects.
|
E1095479
|
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: Saint-Louis | Statement: [Kiki van Eijk, hasWorkedFor, Saint-Louis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saint-Louis Context triple: [Kiki van Eijk, hasWorkedFor, Saint-Louis]
-
A.
Saint-Louis
Saint-Louis is a French border town in the Alsace region, adjacent to Basel and known as a key cross-border transit and commuter hub between France, Switzerland, and Germany.
-
B.
Saint-Louis
Saint-Louis is a historic coastal city in northwestern Senegal that served as a major colonial administrative and trading center in French West Africa.
-
C.
Saint-Louis
Saint-Louis is a coastal commune on the Caribbean island of Marie-Galante, known for its beaches, fishing activities, and traditional Creole character.
-
D.
Place Saint-Louis
Place Saint-Louis is a historic medieval square in Metz, France, known for its arcaded houses and lively cafés.
-
E.
Orléans
Orléans is a federal electoral district in the eastern part of Ottawa, Ontario, represented in the House of Commons of Canada.
- 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: Saint-Louis Triple: [Kiki van Eijk, hasWorkedFor, Saint-Louis]
Generated description
Saint-Louis is a historic French crystal manufacturer renowned for its high-end glassware, lighting, and decorative objects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Saint-Louis Target entity description: Saint-Louis is a historic French crystal manufacturer renowned for its high-end glassware, lighting, and decorative objects.
-
A.
Saint-Louis
Saint-Louis is a French border town in the Alsace region, adjacent to Basel and known as a key cross-border transit and commuter hub between France, Switzerland, and Germany.
-
B.
Saint-Louis
Saint-Louis is a historic coastal city in northwestern Senegal that served as a major colonial administrative and trading center in French West Africa.
-
C.
Saint-Louis
Saint-Louis is a coastal commune on the Caribbean island of Marie-Galante, known for its beaches, fishing activities, and traditional Creole character.
-
D.
Place Saint-Louis
Place Saint-Louis is a historic medieval square in Metz, France, known for its arcaded houses and lively cafés.
-
E.
Orléans
Orléans is a federal electoral district in the eastern part of Ottawa, Ontario, represented in the House of Commons of Canada.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8f519bf881908615f4d47e0f77aa |
completed | April 14, 2026, 7:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c473fa48190866ab946971e971c |
completed | May 8, 2026, 2:36 a.m. |
| NEDg | Description generation | batch_69fd4d56b67c8190bc9ecd4f444df780 |
completed | May 8, 2026, 2:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd4e9a678c8190bd1821e6c43a3a1a |
completed | May 8, 2026, 2:46 a.m. |
Created at: April 10, 2026, 1:15 a.m.