Triple
T11179258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Umberto Nobile |
E264494
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Lauro |
E513705
|
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: Lauro | Statement: [Umberto Nobile, placeOfBirth, Lauro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauro Context triple: [Umberto Nobile, placeOfBirth, Lauro]
-
A.
Lauro
chosen
Lauro is a municipality that serves as its own administrative center, indicating that the town and its governing seat share the same name.
-
B.
Lapa
Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
-
C.
Palmeira
Palmeira is a coastal town on the island of Sal in Cape Verde, known for its fishing harbor and role as a local transport and trade hub.
-
D.
Ciluba
Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
-
E.
Marulanda
Marulanda is a small municipality and town located in the Caldas Department of Colombia, known for its rural Andean landscapes and agricultural economy.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e899c6288190b39e090fa9cdc883 |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4839eeaac8190826007ca126ed491 |
completed | April 19, 2026, 7:26 a.m. |
Created at: April 8, 2026, 9:29 p.m.