Triple
T11192053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vagator Beach |
E264824
|
entity |
| Predicate | hasNearbyTown |
P3883
|
FINISHED |
| Object | Mapusa |
E886372
|
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: Mapusa | Statement: [Vagator Beach, hasNearbyTown, Mapusa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mapusa Context triple: [Vagator Beach, hasNearbyTown, Mapusa]
-
A.
Mapusa
chosen
Mapusa is a bustling commercial town in North Goa, India, known as a major market and transport hub near the popular beaches of the state.
-
B.
Mouraria
Mouraria is a historic Lisbon neighborhood known for its multicultural character, narrow medieval streets, and deep ties to traditional fado music.
-
C.
Morrumbene
Morrumbene is a small town in southern Mozambique known for its rural character within Inhambane Province.
-
D.
Campomoro
Campomoro is a small coastal village and seaside resort on the southwest coast of Corsica, known for its scenic bay and historic Genoese tower.
-
E.
Makarora
Makarora is a small rural settlement in New Zealand’s South Island, known as a gateway to outdoor activities and hiking in the Southern Alps 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_69d6aa9eb9248190b20211772621b4bc |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8be025481909d311b587418dfb2 |
completed | April 9, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ad00f9148190842bf587a2e4cbdf |
completed | April 19, 2026, 10:22 a.m. |
Created at: April 8, 2026, 9:29 p.m.