Triple
T8624622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ousmane Sembène |
E204252
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Ziguinchor |
E605140
|
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: Ziguinchor | Statement: [Ousmane Sembène, placeOfBirth, Ziguinchor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ziguinchor Context triple: [Ousmane Sembène, placeOfBirth, Ziguinchor]
-
A.
Ziguinchor
chosen
Ziguinchor is a major city in southern Senegal, serving as the regional capital of Casamance and an important cultural and economic hub.
-
B.
Guéckédou
Guéckédou is a town in southern Guinea known as a regional trading center near the borders with Sierra Leone and Liberia.
-
C.
Sédhiou
Sédhiou is a town in southern Senegal that serves as an important regional center in the Casamance area.
-
D.
Duékoué
Duékoué is a town in western Côte d'Ivoire that became notorious as a major site of violence and massacres during the country's civil conflicts.
-
E.
Daloa
Daloa is a major inland city in western Côte d'Ivoire known as an important commercial and agricultural center, particularly for cocoa production.
- 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_69ca834a4ea0819094970dceb9e389f3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4728360c8190b5e600596cbced0c |
completed | March 31, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebbe6a7e48190a166a31dccd8ac16 |
completed | April 2, 2026, 6:56 p.m. |
Created at: March 30, 2026, 6:26 p.m.