Triple
T12094394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château Rieussec |
E288031
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Fargues
Fargues is a commune in the Sauternes wine-growing region of southwestern France, renowned for its prestigious sweet white wines.
|
E965657
|
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: Fargues | Statement: [Château Rieussec, locatedIn, Fargues]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fargues Context triple: [Château Rieussec, locatedIn, Fargues]
-
A.
Fargas
Fargas is a surname most notably associated with American actor Antonio Fargas, known for his character roles in film and television.
-
B.
Faya-Largeau
Faya-Largeau is the largest oasis town in northern Chad and an important administrative and trade center in the Sahara Desert.
-
C.
Farges
Farges is a small commune in eastern France, located in the Ain department near the Swiss border in the Auvergne-Rhône-Alpes region.
-
D.
Fallières
Fallières is a French surname most notably borne by Armand Fallières, who served as President of France in the early 20th century.
-
E.
Rousset
Rousset is a French town in the Provence-Alpes-Côte d’Azur region known for hosting significant semiconductor and microelectronics facilities, including a major STMicroelectronics design center.
- 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: Fargues Triple: [Château Rieussec, locatedIn, Fargues]
Generated description
Fargues is a commune in the Sauternes wine-growing region of southwestern France, renowned for its prestigious sweet white wines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fargues Target entity description: Fargues is a commune in the Sauternes wine-growing region of southwestern France, renowned for its prestigious sweet white wines.
-
A.
Fargas
Fargas is a surname most notably associated with American actor Antonio Fargas, known for his character roles in film and television.
-
B.
Faya-Largeau
Faya-Largeau is the largest oasis town in northern Chad and an important administrative and trade center in the Sahara Desert.
-
C.
Farges
Farges is a small commune in eastern France, located in the Ain department near the Swiss border in the Auvergne-Rhône-Alpes region.
-
D.
Fallières
Fallières is a French surname most notably borne by Armand Fallières, who served as President of France in the early 20th century.
-
E.
Rousset
Rousset is a French town in the Provence-Alpes-Côte d’Azur region known for hosting significant semiconductor and microelectronics facilities, including a major STMicroelectronics design center.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91550ce508190babf5755e1553734 |
completed | April 10, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f66edf7881908f29b5b40b9d020f |
completed | May 2, 2026, 1:04 p.m. |
| NEDg | Description generation | batch_69f5fd79da748190b3f0dd7d7a46314d |
completed | May 2, 2026, 1:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f5feeaf2e48190995f282b02a9caaf |
completed | May 2, 2026, 1:40 p.m. |
Created at: April 8, 2026, 9:48 p.m.