Triple
T7432726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jórvík |
E171529
|
entity |
| Predicate | typeOfUrbanSettlement |
P749
|
FINISHED |
| Object | walled town |
—
|
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: walled town | Statement: [Jórvík, typeOfUrbanSettlement, walled town]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfUrbanSettlement Context triple: [Jórvík, typeOfUrbanSettlement, walled town]
-
A.
humanSettlementType
Indicates the classification of a human settlement based on its form or function, such as village, town, or city.
-
B.
urbanAreaType
chosen
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
C.
cityOrTown
Indicates that the subject is classified as a city or a town.
-
D.
typeOfToponym
Indicates the specific category or kind of place name (toponym) that applies to a given geographic entity.
-
E.
metropolitanAreaType
Indicates the classification of a metropolitan area according to its type or category (e.g., size, function, or administrative status).
- 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_69c68a63491881909281f73d4d5643bf |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f325ea908190b668fd4ce646f1e6 |
completed | March 27, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69c6f038582c8190bac77c9b5a34b862 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:12 p.m.