Triple
T16751629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beti–Pahuin languages |
E407094
|
entity |
| Predicate | hasLanguage |
P15
|
FINISHED |
| Object | Ntoum |
E853670
|
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: Ntoum | Statement: [Beti–Pahuin languages, hasLanguage, Ntoum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ntoum Context triple: [Beti–Pahuin languages, hasLanguage, Ntoum]
-
A.
Ntoum
chosen
Ntoum is a town in western Gabon that serves as a growing transport and commercial hub near the capital, Libreville.
-
B.
Meleti
Meleti is a small municipality in the Lombardy region of northern Italy, situated within the Province of Lodi.
-
C.
Nisaea
Nisaea was the port town and harbor of ancient Megara in Greece, serving as its main maritime outlet on the Saronic Gulf.
-
D.
Cibyrrha
Cibyrrha was an ancient coastal city in southwestern Asia Minor, known primarily as the namesake of the Byzantine Cibyrrhaeot naval theme.
-
E.
Usakhelouri
Usakhelouri is a rare, high-quality Georgian red grape variety known for producing naturally semi-sweet, aromatic wines, primarily in the Lechkhumi region.
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3aa271de48190b4a535408aeef734 |
completed | April 18, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a52402848190b029cb0be31b4c74 |
completed | May 10, 2026, 3:32 p.m. |
Created at: April 10, 2026, 5:21 a.m.