Triple
T8938243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borkou |
E212831
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Faya-Largeau |
E263381
|
NE FINISHED |
How this triple was built (2 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: Faya-Largeau | Statement: [Borkou, capital, Faya-Largeau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Faya-Largeau Context triple: [Borkou, capital, Faya-Largeau]
-
A.
Faya-Largeau
chosen
Faya-Largeau is the largest oasis town in northern Chad and an important administrative and trade center in the Sahara Desert.
-
B.
Saussignac
Saussignac is a small wine-producing commune in southwestern France, known for its sweet white wines made primarily from Sémillon and other Bordeaux grape varieties.
-
C.
Vauvert
Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
-
D.
Peseux
Peseux is a former municipality in the canton of Neuchâtel in western Switzerland, now part of the city of Neuchâtel.
-
E.
Douaumont
Douaumont is a small commune in northeastern France best known for its World War I battlefield sites near Verdun, including major memorials and military cemeteries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca839694c88190b324ffeb43d23b08 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66b57a348190979effe4f9998eb7 |
completed | April 1, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cffd8495488190a1d93f9e5b7e334d |
completed | April 3, 2026, 5:48 p.m. |
Created at: March 30, 2026, 6:58 p.m.