Triple
T1735956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 10th arrondissement of Paris |
E37918
|
entity |
| Predicate | hasEthnicallyDiversePopulation |
P1898
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [10th arrondissement of Paris, hasEthnicallyDiversePopulation, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEthnicallyDiversePopulation Context triple: [10th arrondissement of Paris, hasEthnicallyDiversePopulation, true]
-
A.
hasSignificantPopulationGroup
chosen
Indicates that an entity contains or is associated with a notable or substantial subgroup of a population, distinguished by shared characteristics or attributes.
-
B.
hasMinorityIn
Indicates that one entity holds a non-controlling, minority ownership or stake in another entity.
-
C.
involvedEthnicGroup
Indicates that a particular ethnic group participates in, is affected by, or is otherwise associated with the referenced event, situation, or entity.
-
D.
otherEthnicGroup
Indicates that one entity belongs to a different ethnic group than the other entity.
-
E.
affectedEthnicGroup
Indicates that a particular ethnic group is impacted or influenced by an event, condition, policy, or action.
- 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_69a8861cc6ac8190ac0b2e31ccf62851 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab5c553e508190b0f511b05e07fa20 |
completed | March 6, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69aa61c25a648190892de94c997fb983 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.