Triple
T1683049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catshuis |
E36379
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Jacob Cats |
E12930
|
NE 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: Jacob Cats | Statement: [Catshuis, namedAfter, Jacob Cats]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jacob Cats Context triple: [Catshuis, namedAfter, Jacob Cats]
-
A.
Jacob Cats
chosen
Jacob Cats was a prominent 17th-century Dutch poet, jurist, and statesman, best known for his didactic moral verses and influential role in the Dutch Republic.
-
B.
Catz
Catz is the surname of Safra Catz, a prominent business executive best known as the CEO of Oracle Corporation.
-
C.
Catz
Catz is the informal nickname for St Catharine’s College, one of the constituent colleges of the University of Cambridge.
-
D.
Wilbur the Wildcat
Wilbur the Wildcat is the costumed wildcat mascot who represents the University of Arizona at athletic events and campus activities.
-
E.
Los Gatos
Los Gatos is an affluent town in California’s Silicon Valley known for its historic downtown, upscale residential neighborhoods, and proximity to major tech companies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a886139ed081909af0940aa9313512 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa627ab38081909d5f264ca49e036a |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad71bf1998819094d3eb67c6e6bafd |
completed | March 8, 2026, 12:55 p.m. |
Created at: March 4, 2026, 7:29 p.m.