Triple
T7874092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pagalu |
E182807
|
entity |
| Predicate | hasLocalLanguage |
P4185
|
FINISHED |
| Object | Fa d’Ambô |
E182804
|
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: Fa d’Ambô | Statement: [Pagalu, hasLocalLanguage, Fa d’Ambô]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fa d’Ambô Context triple: [Pagalu, hasLocalLanguage, Fa d’Ambô]
-
A.
Fa d’Ambô
chosen
Fa d’Ambô is a Portuguese-based creole language spoken primarily on the island of Annobón in Equatorial Guinea.
-
B.
Amidou
Amidou is a character associated with sorcery and the mystical arts, often depicted in connection with a powerful sorcerer.
-
C.
Fomboni
Fomboni is the largest town and administrative center of the island of Mohéli in the Comoros, known for its coastal setting and role as the island’s main port and commercial hub.
-
D.
Faeto
Faeto is a small town in southern Italy known for its unique linguistic heritage, including the rare Franco-Provençal dialect Faetar.
-
E.
Dyula
Dyula is a Mande language widely used as a trade and lingua franca in parts of West Africa, particularly in Burkina Faso, Côte d’Ivoire, and Mali.
- 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_69ca828a17248190b46defe758bc5ad3 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39a7f1648190980db4db1a800189 |
completed | March 31, 2026, 3:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63a03cc881908253d9cb280c0164 |
completed | April 1, 2026, 12:15 a.m. |
Created at: March 30, 2026, 4:56 p.m.