Triple
T7490687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old City Moat and Walls |
E176996
|
entity |
| Predicate | surroundsArea |
P7850
|
FINISHED |
| Object | approximately 1.6 km by 1.6 km square |
—
|
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 1.6 km by 1.6 km square | Statement: [Old City Moat and Walls, surroundsArea, approximately 1.6 km by 1.6 km square]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: surroundsArea Context triple: [Old City Moat and Walls, surroundsArea, approximately 1.6 km by 1.6 km square]
-
A.
surrounds
chosen
Indicates that one entity is located all around another entity, enclosing or encircling it on multiple sides or completely.
-
B.
surroundedAreaColloquialName
Indicates that an area is commonly referred to by a particular informal or colloquial name by people in the surrounding region.
-
C.
enclosesArea
Indicates that one entity surrounds and contains a bounded region of space occupied or defined by another.
-
D.
representedArea
Indicates that one entity serves as a representation or depiction of a particular area or region.
-
E.
coreTerritoryAround
Indicates that one entity serves as the central or primary territory surrounding or enclosing another entity.
- 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_69c69f2583808190bd1a4936c42a5815 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f81b431481908214b69c6c8d83bc |
completed | March 27, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d266d88190982cf5d2ee2e9564 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:43 p.m.