Triple
T10247488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Eyeghé Ndong |
E240253
|
entity |
| Predicate | languageSpoken |
P151
|
FINISHED |
| Object | Fang |
E56340
|
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: Fang | Statement: [Jean Eyeghé Ndong, languageSpoken, Fang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fang Context triple: [Jean Eyeghé Ndong, languageSpoken, Fang]
-
A.
Fang
Fang is a mysterious, dark-winged member of the avian-human hybrid "flock" and Max's closest ally and love interest in James Patterson's Maximum Ride series.
-
B.
Fang
Fang is Rubeus Hagrid’s large, cowardly boarhound (often called a dog) from the Harry Potter series.
-
C.
Fang
chosen
Fang is a Bantu language widely spoken by the Fang people of Central Africa, particularly in Equatorial Guinea, Gabon, and Cameroon.
-
D.
Fong
Fong is a romanized surname and given name, commonly used in Cantonese-speaking communities as a variant transliteration of the Chinese name Feng.
-
E.
Shachi
Shachi, also known as Indrani, is the queen of the gods and the goddess of beauty and prosperity in Hindu mythology, revered as the wife of the god Indra.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d22e0d4c8190a6712859924e9d3d |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f7ade8448190830d950b7cee0c34 |
completed | April 9, 2026, 12:49 a.m. |
Created at: April 6, 2026, 11:27 a.m.