Triple
T19468087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yei |
E487050
|
entity |
| Predicate | hasRoadConnectionTo |
P11435
|
FINISHED |
| Object | Lainya |
—
|
NE NERFINISHED |
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: Lainya | Statement: [Yei, hasRoadConnectionTo, Lainya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lainya Context triple: [Yei, hasRoadConnectionTo, Lainya]
-
A.
Lainya
chosen
Lainya is a town in South Sudan that serves as one of the key urban centers in Central Equatoria State.
-
B.
Lia
Lia is a Japanese singer best known for performing iconic anime theme songs, including the opening of Angel Beats!.
-
C.
Lia
Lia is a central character in Umberto Eco's novel "Foucault's Pendulum," serving as a grounding, humanizing presence amid the book's dense intellectual conspiracies and esoteric themes.
-
D.
Ta’aisha
The Ta’aisha are a Sudanese Arab tribal group from the Darfur–Kordofan region, historically prominent through their leadership role in the Mahdist state under Abdallahi ibn Muhammad.
-
E.
Laleia
Laleia is a town in northern Timor-Leste known as the birthplace of independence leader and former president Xanana Gusmão.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633e4b230819097c8804ee91988ea |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:39 p.m.