Triple
T15110575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Art and History |
E360899
|
entity |
| Predicate | labelHolders |
P117361
|
FINISHED |
| Object | French municipalities |
—
|
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 municipalities | Statement: [City of Art and History, labelHolders, French municipalities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: labelHolders Context triple: [City of Art and History, labelHolders, French municipalities]
-
A.
labelOf
Indicates that one entity serves as the name, tag, or identifying label assigned to another entity.
-
B.
titleHolders
Indicates that one or more entities currently or formerly hold a specified title, position, or honor.
-
C.
titleHoldersWere
Indicates that certain entities previously held a specified title or position during a past period.
-
D.
labelCatalog
Indicates assigning or associating a descriptive label or identifier with a catalog entity or catalog entry.
-
E.
labelMate
Indicates that two entities are associated with the same label, such as being signed to or represented by the same organization, brand, or record label.
- 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_69d85a0491ec8190830960be8fafb994 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0058c04f481909deeac0271d961b6 |
completed | April 15, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69deb96c1d9c81909351558ed97bc5b7 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec71e8dcc81908badc834b6ccf273 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:05 a.m.