Triple
T10257541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Têt River |
E240510
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object |
Millas
Millas is a commune in the Pyrénées-Orientales department of southern France, situated in the Roussillon plain near Perpignan.
|
E854125
|
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: Millas | Statement: [Têt River, passesThrough, Millas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Millas Context triple: [Têt River, passesThrough, Millas]
-
A.
Tagüeña
Tagüeña is a Spanish surname most notably associated with Manuel Tagüeña, a Republican military officer and physicist active during the Spanish Civil War.
-
B.
Alpedrete
Alpedrete is a municipality in the Community of Madrid, Spain, known for its traditional stone quarries and residential character near the Sierra de Guadarrama.
-
C.
Santarosa
Santarosa was an Italian nobleman and revolutionary best known for his support of Greek independence and his role among the Philhellenes in the Greek War of Independence.
-
D.
Sacaba
Sacaba is a Bolivian city in the Cochabamba metropolitan area known for its agricultural production and traditional festivals.
-
E.
Navarro
Navarro is a Spanish surname borne by numerous notable individuals across fields such as film, sports, politics, and academia.
- 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: Millas Triple: [Têt River, passesThrough, Millas]
Generated description
Millas is a commune in the Pyrénées-Orientales department of southern France, situated in the Roussillon plain near Perpignan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Millas Target entity description: Millas is a commune in the Pyrénées-Orientales department of southern France, situated in the Roussillon plain near Perpignan.
-
A.
Tagüeña
Tagüeña is a Spanish surname most notably associated with Manuel Tagüeña, a Republican military officer and physicist active during the Spanish Civil War.
-
B.
Alpedrete
Alpedrete is a municipality in the Community of Madrid, Spain, known for its traditional stone quarries and residential character near the Sierra de Guadarrama.
-
C.
Santarosa
Santarosa was an Italian nobleman and revolutionary best known for his support of Greek independence and his role among the Philhellenes in the Greek War of Independence.
-
D.
Sacaba
Sacaba is a Bolivian city in the Cochabamba metropolitan area known for its agricultural production and traditional festivals.
-
E.
Navarro
Navarro is a Spanish surname borne by numerous notable individuals across fields such as film, sports, politics, and academia.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d24de4588190b68fb3daa36dbd7d |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f7e153b0819084708b6f7127cdea |
completed | April 9, 2026, 12:50 a.m. |
| NEDg | Description generation | batch_69d6fcab0bfc8190b47bc165ef3eb15d |
completed | April 9, 2026, 1:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d70fc3b15081908d1b67a7094c6210 |
completed | April 9, 2026, 2:32 a.m. |
Created at: April 6, 2026, 11:31 a.m.