Triple
T3512329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Musée d’Histoire de Marseille |
E74223
|
entity |
| Predicate | hasNumberOfObjects |
P48434
|
FINISHED |
| Object | tens of thousands of items |
—
|
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: tens of thousands of items | Statement: [Musée d’Histoire de Marseille, hasNumberOfObjects, tens of thousands of items]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfObjects Context triple: [Musée d’Histoire de Marseille, hasNumberOfObjects, tens of thousands of items]
-
A.
hasObject
Indicates that an entity is associated with or possesses a particular object as part of a relationship or action.
-
B.
hasNumberOfRows
Indicates the specific count of rows associated with or contained in an entity.
-
C.
hasComponentCount
Indicates that an entity is associated with a specific number of components it contains or comprises.
-
D.
hasNumberOfPoints
Indicates that an entity is associated with a specific count of points it possesses or comprises.
-
E.
hasNumberOfFields
Indicates the specific count of fields or distinct data elements that an entity possesses.
- 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_69ad85cfb5c881909c9a2edd9d6043cc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc10b6b48190bedfed6d34afc425 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0e770481908528fa35eda53003 |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adaed74ecc8190b74dc70ab59a3e1c |
completed | March 8, 2026, 5:16 p.m. |
Created at: March 8, 2026, 3:19 p.m.