Triple
T26937169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinese Cuban community in Havana |
E678417
|
entity |
| Predicate | mainCommercialArea |
P127310
|
FINISHED |
| Object | Chinatown, Havana |
—
|
NE NERFINISHED |
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: Chinatown, Havana | Statement: [Chinese Cuban community in Havana, mainCommercialArea, Chinatown, Havana]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCommercialArea Context triple: [Chinese Cuban community in Havana, mainCommercialArea, Chinatown, Havana]
-
A.
commercialArea
Indicates that the location or region is designated primarily for commercial activities such as businesses, shops, or services.
-
B.
isPrimaryCommercialAreaOf
chosen
Indicates that one area serves as the main center of commercial activity for another specified place or region.
-
C.
connectsCommercialAreas
Indicates a relationship where one entity links or provides direct access between two or more commercial areas or business districts.
-
D.
connectsToCommercialArea
Indicates that one location has a direct link, route, or access path to a commercial area.
-
E.
hasCommercialCenterType
Indicates that an entity has or is associated with a specific type or category of commercial center (e.g., mall, shopping district, business park).
- F. None of above.
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_69eeeb4d69588190a7c912164a1c37b3 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6a28c7c148190bfc980aad9f678ca |
completed | May 3, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69f69fe1e3c88190830bb2e9f407357e |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 27, 2026, 6:16 a.m.