Triple
T14844316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gillingham |
E349045
|
entity |
| Predicate | nearbyTown |
P3883
|
FINISHED |
| Object | Mere |
E547023
|
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: Mere | Statement: [Gillingham, nearbyTown, Mere]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mere Context triple: [Gillingham, nearbyTown, Mere]
-
A.
Mere
chosen
Mere is a village and civil parish in Cheshire, England, known for its affluent residential character and proximity to the town of Knutsford.
-
B.
Mere
Mere is a small historic market town in southwest Wiltshire, England, known for its rural setting near the borders of Dorset and Somerset.
-
C.
Mera
Mera is a powerful Atlantean warrior and sorceress from DC Comics, best known as Aquaman’s ally and queen of Atlantis.
-
D.
Mera
Mera is a town in Ecuador that serves as the administrative and commercial center of Mera Canton in Pastaza Province.
-
E.
Ménaka
Ménaka is a town in eastern Mali that serves as an important administrative and trading center in the Sahel 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded291103c8190a64cfe700bfee197 |
completed | April 14, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe64fe89e88190912cd205feef85d3 |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:53 a.m.