Triple
T4131936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guéckédou Prefecture |
E85059
|
entity |
| Predicate | locatedInCountryCapitalLanguageContext |
P54022
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Guéckédou Prefecture, locatedInCountryCapitalLanguageContext, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInCountryCapitalLanguageContext Context triple: [Guéckédou Prefecture, locatedInCountryCapitalLanguageContext, French]
-
A.
locatedInCountryCapitalSystem
Indicates that an entity is situated in the capital city of a specified country within a country–capital relationship system.
-
B.
locatedInCountryCapitalOfProvince
Indicates that an entity is located in the country that contains the capital city of a specified province.
-
C.
countryCapitalLocatedOn
Indicates that the capital city of a country is situated on or directly adjacent to a specified geographic feature (such as a river, coast, or lake).
-
D.
refersToCapitalCountry
Indicates that one entity is the capital city of the country represented by the other entity.
-
E.
locatedInCapital
Indicates that an entity is situated within the capital city of a specified region, country, or administrative area.
- 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_69aed935ccd881909dc61f81bcdb7a78 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af03a0f3408190adba7a8513bd3d12 |
completed | March 9, 2026, 5:30 p.m. |
| PD | Predicate disambiguation | batch_69af01883b6c8190a482ead589a131a5 |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af039fb19c8190b20e62a3b3ad25c1 |
completed | March 9, 2026, 5:30 p.m. |
Created at: March 9, 2026, 3:42 p.m.