Triple
T184306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herengracht |
E3946
|
entity |
| Predicate | hasNotableSection |
P3574
|
FINISHED |
| Object | area between Vijzelstraat and Leidsestraat |
—
|
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: area between Vijzelstraat and Leidsestraat | Statement: [Herengracht, hasNotableSection, area between Vijzelstraat and Leidsestraat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableSection Context triple: [Herengracht, hasNotableSection, area between Vijzelstraat and Leidsestraat]
-
A.
hasNotableSegment
chosen
Indicates that an entity includes or contains a specific segment, part, or portion that is considered notable or significant in some way.
-
B.
hasNotableFeature
Indicates that an entity possesses a specific characteristic, trait, or attribute that is considered significant or noteworthy.
-
C.
hasNotableSubject
Indicates that an entity is associated with a subject that is particularly significant, prominent, or noteworthy in relation to it.
-
D.
hasNotableWord
Indicates that an entity is associated with a word or term that is considered notable, distinctive, or significant in some context.
-
E.
hasSectionCount
Indicates that an entity is associated with a specific number of sections it contains or comprises.
- F. None of above.
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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25926be9c8190a4cfce66f57589d1 |
completed | Feb. 28, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69a2566fb08c81908faff2fde552105d |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.