Triple
T3853325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agly |
E85349
|
entity |
| Predicate | flowsNear |
P350
|
FINISHED |
| Object |
Rivesaltes
Rivesaltes is a commune in southern France’s Pyrénées-Orientales department, known for its wine production and historical internment camp.
|
E392494
|
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: Rivesaltes | Statement: [Agly, flowsNear, Rivesaltes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rivesaltes Context triple: [Agly, flowsNear, Rivesaltes]
-
A.
Lavezares
Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
-
B.
Eygues
Eygues is a river in southeastern France that flows through the Drôme department before joining the larger Rhône basin.
-
C.
Alès
Alès is a historic industrial town in southern France, located at the foot of the Cévennes mountains.
-
D.
Manosque
Manosque is a historic town in southeastern France’s Provence region, known for its medieval old town, surrounding lavender fields, and proximity to the Luberon mountains.
-
E.
Pézenas
Pézenas is a historic town in southern France’s Hérault department, known for its well-preserved medieval center and association with figures like Molière and the Prince de Conti.
- 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: Rivesaltes Triple: [Agly, flowsNear, Rivesaltes]
Generated description
Rivesaltes is a commune in southern France’s Pyrénées-Orientales department, known for its wine production and historical internment camp.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rivesaltes Target entity description: Rivesaltes is a commune in southern France’s Pyrénées-Orientales department, known for its wine production and historical internment camp.
-
A.
Lavezares
Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
-
B.
Eygues
Eygues is a river in southeastern France that flows through the Drôme department before joining the larger Rhône basin.
-
C.
Alès
Alès is a historic industrial town in southern France, located at the foot of the Cévennes mountains.
-
D.
Manosque
Manosque is a historic town in southeastern France’s Provence region, known for its medieval old town, surrounding lavender fields, and proximity to the Luberon mountains.
-
E.
Pézenas
Pézenas is a historic town in southern France’s Hérault department, known for its well-preserved medieval center and association with figures like Molière and the Prince de Conti.
- 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_69aed936de1c81908f91bed80f70abb2 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec0438308190865ff74bee5a1cf2 |
completed | March 9, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5041c7250819093b2743afeb6e36c |
completed | March 14, 2026, 6:45 a.m. |
| NEDg | Description generation | batch_69b504c46dcc8190a9775c39e5c734a9 |
completed | March 14, 2026, 6:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b505742830819093a861bde17c03c0 |
completed | March 14, 2026, 6:51 a.m. |
Created at: March 9, 2026, 3:19 p.m.