Triple
T15544757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cámara (Argentine Navy officer) |
E370574
|
entity |
| Predicate | nameInEnglish |
P3437
|
FINISHED |
| Object |
Cámara
Cámara is an Argentine Navy officer known for his service in Argentina's naval forces.
|
E1162960
|
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: Cámara | Statement: [Cámara (Argentine Navy officer), nameInEnglish, Cámara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cámara Context triple: [Cámara (Argentine Navy officer), nameInEnglish, Cámara]
-
A.
Camira
Camira is a residential suburb located within the Ipswich City Council area in South East Queensland, Australia.
-
B.
Camira
Camira is a compact family car model produced by Holden, the Australian subsidiary of General Motors, during the 1980s.
-
C.
The Camera
The Camera is a seminal photography book by Ansel Adams that explores the technical and artistic use of cameras in creating expressive photographs.
-
D.
Cámara Base
Cámara Base is an Argentine research station in Antarctica that operates primarily during the austral summer to support scientific and logistical activities in the region.
-
E.
Caméra One
Caméra One is a French film production company known for producing acclaimed art-house and auteur-driven movies.
- 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: Cámara Triple: [Cámara (Argentine Navy officer), nameInEnglish, Cámara]
Generated description
Cámara is an Argentine Navy officer known for his service in Argentina's naval forces.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cámara Target entity description: Cámara is an Argentine Navy officer known for his service in Argentina's naval forces.
-
A.
Camira
Camira is a residential suburb located within the Ipswich City Council area in South East Queensland, Australia.
-
B.
Camira
Camira is a compact family car model produced by Holden, the Australian subsidiary of General Motors, during the 1980s.
-
C.
The Camera
The Camera is a seminal photography book by Ansel Adams that explores the technical and artistic use of cameras in creating expressive photographs.
-
D.
Cámara Base
Cámara Base is an Argentine research station in Antarctica that operates primarily during the austral summer to support scientific and logistical activities in the region.
-
E.
Caméra One
Caméra One is a French film production company known for producing acclaimed art-house and auteur-driven movies.
- 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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e0443410408190a249889edcd9c599 |
completed | April 16, 2026, 2:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff455a38188190a593c70be09d6103 |
completed | May 9, 2026, 2:31 p.m. |
| NEDg | Description generation | batch_69ff45dbc9dc8190b3cac64e4a418aa3 |
completed | May 9, 2026, 2:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff464571808190bfe7ef3a33d246f1 |
completed | May 9, 2026, 2:35 p.m. |
Created at: April 10, 2026, 4:07 a.m.