Triple
T34684305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Free Imperial City of Dillenburg |
E890698
|
entity |
| Predicate | typeOfCity |
P3206
|
FINISHED |
| Object | imperial city |
—
|
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: imperial city | Statement: [Free Imperial City of Dillenburg, typeOfCity, imperial city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCity Context triple: [Free Imperial City of Dillenburg, typeOfCity, imperial city]
-
A.
cityGroupType
Indicates the classification or category type assigned to a group of cities within a larger organizational or geographic structure.
-
B.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
-
C.
city1
chosen
Indicates that the subject is classified as a city.
-
D.
cityIs
Indicates that one entity is a city associated with, or identified as, another 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_69f349dabc008190a18999c26682ed47 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a0154b16a3c8190b808a460ae3a91ac |
completed | May 11, 2026, 4:01 a.m. |
| PD | Predicate disambiguation | batch_6a01539f19d88190b2de63f5262b1074 |
completed | May 11, 2026, 3:57 a.m. |
Created at: May 1, 2026, 2:05 a.m.