Triple
T12871373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Algeria |
E307856
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Saida
Saida is a city in northwestern Algeria known as an important regional center for agriculture, trade, and transportation.
|
E1006375
|
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: Saida | Statement: [Northern Algeria, contains, Saida]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saida Context triple: [Northern Algeria, contains, Saida]
-
A.
Saida
Saida is a historic coastal city in southern Lebanon known for its ancient Phoenician heritage, bustling seaport, and well-preserved archaeological sites.
-
B.
Nago
Nago is a coastal city in northern Okinawa, Japan, known for its beaches, subtropical climate, and role as a regional commercial and cultural center.
-
C.
Suwawa
Suwawa is an Austronesian language spoken by the Suwawa people in the northern part of Sulawesi, Indonesia.
-
D.
Kasaba
Kasaba is a 1997 Turkish drama film by acclaimed director Nuri Bilge Ceylan, noted for its quiet, contemplative portrayal of rural family life and childhood.
-
E.
Hita
Hita is a historic town in the province of Guadalajara, Spain, known for its medieval architecture and literary associations.
- 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: Saida Triple: [Northern Algeria, contains, Saida]
Generated description
Saida is a city in northwestern Algeria known as an important regional center for agriculture, trade, and transportation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Saida Target entity description: Saida is a city in northwestern Algeria known as an important regional center for agriculture, trade, and transportation.
-
A.
Saida
Saida is a historic coastal city in southern Lebanon known for its ancient Phoenician heritage, bustling seaport, and well-preserved archaeological sites.
-
B.
Nago
Nago is a coastal city in northern Okinawa, Japan, known for its beaches, subtropical climate, and role as a regional commercial and cultural center.
-
C.
Suwawa
Suwawa is an Austronesian language spoken by the Suwawa people in the northern part of Sulawesi, Indonesia.
-
D.
Kasaba
Kasaba is a 1997 Turkish drama film by acclaimed director Nuri Bilge Ceylan, noted for its quiet, contemplative portrayal of rural family life and childhood.
-
E.
Hita
Hita is a historic town in the province of Guadalajara, Spain, known for its medieval architecture and literary associations.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970905784819091631161a9de98c5 |
completed | April 10, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69bb4b28c8190a4ec9cad4e1e0f05 |
completed | May 3, 2026, 12:49 a.m. |
| NEDg | Description generation | batch_69f69cc60c488190a5a71e25c075e9ff |
completed | May 3, 2026, 12:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69d845a9081909b40562825c1c500 |
completed | May 3, 2026, 12:57 a.m. |
Created at: April 9, 2026, 5:38 p.m.