Triple
T10619605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amay |
E295673
|
entity |
| Predicate | hasMunicipalSection |
P10450
|
FINISHED |
| Object | Amay (section) |
E295673
|
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: Amay (section) | Statement: [Amay, hasMunicipalSection, Amay (section)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amay (section) Context triple: [Amay, hasMunicipalSection, Amay (section)]
-
A.
Amay
chosen
Amay is a municipality in the Walloon Region of Belgium, located in the province of Liège along the Meuse River.
-
B.
Ameya-Yokochō
Ameya-Yokochō is a bustling open-air market street in Tokyo known for its dense concentration of shops, food stalls, and bargain goods.
-
C.
Amat
Amat is a surname most notably borne by Pol Amat, a renowned Spanish field hockey player considered one of the sport’s greats.
-
D.
Akaiami
Akaiami is a small, picturesque islet in the Aitutaki Lagoon of the Cook Islands, known for its white-sand beaches and clear turquoise waters.
-
E.
Anejima
Anejima is a small, uninhabited Japanese island that forms part of the remote Mukojima subgroup in the Ogasawara (Bonin) Islands chain.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d6df6fc47c8190b77b61a7fd223d65 |
completed | April 8, 2026, 11:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96b86a5bc8190863034cc91fdbbb9 |
completed | April 10, 2026, 9:28 p.m. |
Created at: April 8, 2026, 8:21 p.m.