Triple
T13363202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meru |
E318870
|
entity |
| Predicate | roadConnection |
P385
|
FINISHED |
| Object | Embu |
E318869
|
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: Embu | Statement: [Meru, roadConnection, Embu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Embu Context triple: [Meru, roadConnection, Embu]
-
A.
Embu
chosen
Embu is a town in central Kenya that serves as a commercial and administrative hub on the southeastern slopes of Mount Kenya.
-
B.
Ciluba
Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
-
C.
Corumbá
Corumbá is a Brazilian city in the state of Mato Grosso do Sul, known as a key gateway to the Pantanal wetlands and an important regional center for river trade and ecotourism.
-
D.
Itatiba
Itatiba is a municipality in southeastern Brazil known for its quality of life and proximity to the metropolitan region of Campinas in the state of São Paulo.
-
E.
Bientina
Bientina is a small Tuscan town in central Italy known for its historic center and location within the Province of Pisa.
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69da628affd081909f1790d333f0eef4 |
completed | April 11, 2026, 3:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7267c99788190b158b1d9f57ceba2 |
completed | May 3, 2026, 10:42 a.m. |
Created at: April 9, 2026, 9:32 p.m.