Triple
T34683729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Free Imperial City of Biberach |
E890684
|
entity |
| Predicate | hadUrbanFortifications |
P164891
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Free Imperial City of Biberach, hadUrbanFortifications, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadUrbanFortifications Context triple: [Free Imperial City of Biberach, hadUrbanFortifications, yes]
-
A.
eraOfFortification
Indicates the historical time period during which a structure or site was fortified or equipped with defensive works.
-
B.
hadCityWalls
chosen
Indicates that a city or settlement possessed defensive walls surrounding it.
-
C.
wasFortifiedBy
Indicates that a place or structure received defensive strengthening or fortification through the actions or resources of a specified agent or entity.
-
D.
usedFortificationsAt
Indicates that an entity employed or deployed specific fortifications in a particular context, such as a battle, campaign, or defensive situation.
-
E.
earliestRecordedFortification
Indicates that the subject is the earliest known or first historically recorded example of a particular fortification associated with the object.
- 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_69f349dabc008190a18999c26682ed47 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7232a16a081909b775efc60b45244 |
completed | May 3, 2026, 10:27 a.m. |
| PD | Predicate disambiguation | batch_69f72157af108190880317a62e634bb0 |
completed | May 3, 2026, 10:20 a.m. |
Created at: May 1, 2026, 2:05 a.m.