Triple
T10362178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Catalonia |
E244163
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Ceret
Ceret is a historic town in southern France’s Pyrénées-Orientales, renowned for its modern art museum and strong Catalan cultural heritage.
|
E858718
|
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: Ceret | Statement: [Northern Catalonia, contains, Ceret]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ceret Context triple: [Northern Catalonia, contains, Ceret]
-
A.
Cérons
Cérons is a French wine appellation in the Graves region of Bordeaux, known for its sweet white wines made primarily from Semillon, Sauvignon Blanc, and Muscadelle grapes.
-
B.
Cèze
The Cèze is a river in southern France known for its scenic gorges, clear waters, and popular swimming and canoeing spots.
-
C.
Lavezares
Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
-
D.
Mouthe
Mouthe is a commune in eastern France’s Jura Mountains, known for its harsh winters and extremely low temperatures.
-
E.
Rivesaltes
Rivesaltes is a commune in southern France’s Pyrénées-Orientales department, known for its wine production and historical internment camp.
- 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: Ceret Triple: [Northern Catalonia, contains, Ceret]
Generated description
Ceret is a historic town in southern France’s Pyrénées-Orientales, renowned for its modern art museum and strong Catalan cultural heritage.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ceret Target entity description: Ceret is a historic town in southern France’s Pyrénées-Orientales, renowned for its modern art museum and strong Catalan cultural heritage.
-
A.
Cérons
Cérons is a French wine appellation in the Graves region of Bordeaux, known for its sweet white wines made primarily from Semillon, Sauvignon Blanc, and Muscadelle grapes.
-
B.
Cèze
The Cèze is a river in southern France known for its scenic gorges, clear waters, and popular swimming and canoeing spots.
-
C.
Lavezares
Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
-
D.
Mouthe
Mouthe is a commune in eastern France’s Jura Mountains, known for its harsh winters and extremely low temperatures.
-
E.
Rivesaltes
Rivesaltes is a commune in southern France’s Pyrénées-Orientales department, known for its wine production and historical internment camp.
- 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e962f08c8190a7ac489dc524510d |
completed | April 7, 2026, 11:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d750bcf00081909b44ffa5df76aec1 |
completed | April 9, 2026, 7:09 a.m. |
| NEDg | Description generation | batch_69d7618ecb748190a492406eabe590d7 |
completed | April 9, 2026, 8:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d77057affc8190b420e66560c3dfbd |
completed | April 9, 2026, 9:24 a.m. |
Created at: April 6, 2026, 11:59 a.m.