Triple
T28312599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Musée Condé |
E714038
|
entity |
| Predicate | collectionRankInFrance |
P179482
|
FINISHED |
| Object | second after the Louvre for paintings and drawings |
—
|
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: second after the Louvre for paintings and drawings | Statement: [Musée Condé, collectionRankInFrance, second after the Louvre for paintings and drawings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: collectionRankInFrance Context triple: [Musée Condé, collectionRankInFrance, second after the Louvre for paintings and drawings]
-
A.
economicRankInFrance
Indicates the relative economic standing or ranking of an entity within the context of France’s economy.
-
B.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
-
C.
ratingFrance
Indicates that an entity assigns or holds a rating or evaluation specifically related to France.
-
D.
chartPositionFrenchSinglesChart
Indicates the position an item holds on the French singles music chart.
-
E.
FranceScore
Indicates the score or points achieved by France in a given event, match, or context.
- 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_69efb5256afc8190b9322d25c3ae6320 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f722c1bc648190a79bfdc722dcaaa4 |
completed | May 3, 2026, 10:26 a.m. |
| PD | Predicate disambiguation | batch_69f72153a9188190b02adc84e1be4af8 |
completed | May 3, 2026, 10:20 a.m. |
| PDg | Predicate description generation | batch_69f7221bc57c819085c1464a45e61b2f |
completed | May 3, 2026, 10:23 a.m. |
Created at: April 27, 2026, 11:41 p.m.