Triple
T204207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caroline von Humboldt |
E4573
|
entity |
| Predicate | notablePlace |
P10233
|
FINISHED |
| Object | Berlin salons |
—
|
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: Berlin salons | Statement: [Caroline von Humboldt, notablePlace, Berlin salons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notablePlace Context triple: [Caroline von Humboldt, notablePlace, Berlin salons]
-
A.
notableLocation
Indicates that a location is especially significant, prominent, or noteworthy in relation to the subject.
-
B.
notableSite
Indicates that a site holds particular significance, prominence, or recognition in some context.
-
C.
touristAttractionIn
Indicates that a place functions as a tourist attraction located within a specified geographic area or entity.
-
D.
significantMonument
Indicates that something is a monument of notable historical, cultural, or symbolic importance.
-
E.
notableFor
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
- 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25f46b4f081909e5ee3718109a71f |
completed | Feb. 28, 2026, 3:21 a.m. |
| PD | Predicate disambiguation | batch_69a25b4b42ec8190bef16bbbdd30a742 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25f4602c081909e89de233cbc5670 |
completed | Feb. 28, 2026, 3:21 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.