Triple
T12565574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lampa Province |
E295468
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Lampa |
E145225
|
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: Lampa | Statement: [Lampa Province, capital, Lampa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lampa Context triple: [Lampa Province, capital, Lampa]
-
A.
Lampa
chosen
Lampa is a commune and town in central Chile known for its semi-rural character and growing residential and industrial development near Santiago.
-
B.
Lampada Ferens
Lampada Ferens is the Latin motto of the University of Hull, traditionally translated as "carrying the light" or "bearing the lamp."
-
C.
Lampione
Lampione is a tiny, uninhabited rocky islet in the Mediterranean Sea, part of Italy’s Pelagie Islands and known for its rich marine life and popular diving spots.
-
D.
Abajur
Abajur is a conceptual artwork by Brazilian artist Cildo Meireles, known for its exploration of perception, space, and political or social commentary through immersive, often installation-based forms.
-
E.
Lumo
Lumo is a British open-access train operator running low-cost, long-distance electric services on the East Coast Main Line between London and northeastern England.
- 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9549611c081909e611756f3cce7f0 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6558f87b081909ba179b49bae3913 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 11:49 p.m.