Triple
T5882008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nizamiyya of Baghdad |
E130769
|
entity |
| Predicate | cityTypeContext |
P749
|
FINISHED |
| Object | capital of the Abbasid Caliphate |
—
|
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: capital of the Abbasid Caliphate | Statement: [Nizamiyya of Baghdad, cityTypeContext, capital of the Abbasid Caliphate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityTypeContext Context triple: [Nizamiyya of Baghdad, cityTypeContext, capital of the Abbasid Caliphate]
-
A.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
-
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.
city1
Indicates that the subject is classified as a city.
-
D.
cityServedType
Indicates the type or category of city that is served by a given entity (such as a facility, service, or infrastructure).
-
E.
cityName
Indicates that the associated value is the name of a city.
- 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03fe07b7081909f8577ec3a9a1a8d |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0334bdc308190ad0d7199ab975588 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:57 p.m.