Triple
T28569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris |
E568
|
entity |
| Predicate | administrativeDivision |
P747
|
FINISHED |
| Object | 20 arrondissements |
—
|
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: 20 arrondissements | Statement: [Paris, administrativeDivision, 20 arrondissements]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: administrativeDivision Context triple: [Paris, administrativeDivision, 20 arrondissements]
-
A.
countrySubdivision
Indicates that one geopolitical region is an administrative or territorial subdivision of a larger country.
-
B.
hasAdministrativeCenter
Indicates that an administrative unit (such as a region, district, or municipality) has a specific place designated as its main governing or administrative center.
-
C.
divisionTitle
Indicates the formal name or title assigned to a specific division within a larger organization or structure.
-
D.
hasSubdivision
chosen
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
E.
federalDivision
Indicates that one entity is a federal-level administrative or political subdivision (such as a state, province, or territory) within the jurisdiction of another entity.
- 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_69a2479dec388190967ba648663442c9 |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24925607c8190a9ce7ec834f3e5bb |
completed | Feb. 28, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69a2486bd74c81908d32be3c7d22f51f |
completed | Feb. 28, 2026, 1:44 a.m. |
Created at: Feb. 28, 2026, 1:44 a.m.