Triple
T20204151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cao Bang Province |
E493303
|
entity |
| Predicate | wasFrenchOutpost |
P139182
|
FINISHED |
| Object | Cao Bang French garrison |
—
|
LITERAL 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: Cao Bang French garrison | Statement: [Cao Bang Province, wasFrenchOutpost, Cao Bang French garrison]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasFrenchOutpost Context triple: [Cao Bang Province, wasFrenchOutpost, Cao Bang French garrison]
-
A.
primaryFrenchDestination
Indicates that one entity is the main or most significant travel destination in France for another entity.
-
B.
FrenchSide
Indicates that an entity is positioned on, associated with, or belongs to the French side of a border, division, or relationship.
-
C.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
-
D.
borderTownOnFrenchSide
Indicates that a town is located on the French side of a border shared with another country.
-
E.
usesPrimaryFrenchGateway
Indicates that an entity routes its primary communications or connections through a main gateway located in or associated with French infrastructure or networks.
- F. None of above. chosen
Provenance (4 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66d8f90108190b72e37c0056de0f8 |
completed | April 20, 2026, 6:16 p.m. |
| PD | Predicate disambiguation | batch_69e55b14c9d8819095453d0504d9222f |
completed | April 19, 2026, 10:45 p.m. |
| PDg | Predicate description generation | batch_69e56700b1a08190ace53cf95827d72d |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 11, 2026, 11:38 p.m.