Triple
T15501745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stelling van Amsterdam |
E378973
|
entity |
| Predicate | numberOfForts |
P118898
|
FINISHED |
| Object | approximately 46 |
—
|
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: approximately 46 | Statement: [Stelling van Amsterdam, numberOfForts, approximately 46]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfForts Context triple: [Stelling van Amsterdam, numberOfForts, approximately 46]
-
A.
numberOfBastions
Indicates the quantity of bastions associated with or contained by a given entity.
-
B.
numberOfTowers
Indicates the quantity of towers associated with or contained by a given entity.
-
C.
hasFortifications
Indicates that one entity possesses or is equipped with defensive structures or fortification works associated with it.
-
D.
containsFortress
Indicates that a location or area includes a fortress within its boundaries.
-
E.
usedFortificationsAt
Indicates that an entity employed or deployed specific fortifications in a particular context, such as a battle, campaign, or defensive situation.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcc5bb88190b8a9a81419a9a38b |
completed | April 16, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69ded2896a9c8190a8b9627deb3c17b4 |
completed | April 14, 2026, 11:49 p.m. |
| PDg | Predicate description generation | batch_69ded57165288190979b7acb71ad5145 |
completed | April 15, 2026, 12:01 a.m. |
Created at: April 10, 2026, 3:54 a.m.