Triple
T15581579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ancien Régime in Paris |
E374511
|
entity |
| Predicate | hasEliteLanguage |
P119293
|
FINISHED |
| Object | standard 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: standard French | Statement: [Ancien Régime in Paris, hasEliteLanguage, standard French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEliteLanguage Context triple: [Ancien Régime in Paris, hasEliteLanguage, standard French]
-
A.
hasMemberLanguage
Indicates that one entity is a language that is a constituent or member of a larger language group, family, or collection represented by the other entity.
-
B.
eligibleLanguage
Indicates that a particular language satisfies the required conditions to be considered valid or allowed in a given context.
-
C.
hasStrongLanguage
Indicates that the subject contains or uses intense, offensive, or explicit language.
-
D.
hasOfficerLanguage
Indicates that an officer is able or authorized to communicate in a specified language.
-
E.
hasLanguageStatus
Indicates that an entity has a particular status or condition regarding its language use, recognition, or classification.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e45ee3c8190a6aee06a5805ca39 |
completed | April 16, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69deda817e9881909b0c66fc9056f7d5 |
completed | April 15, 2026, 12:23 a.m. |
| PDg | Predicate description generation | batch_69dff7f05f708190850f1d8782e132b0 |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 10, 2026, 4:11 a.m.