Triple
T1483201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mit Abu al-Kum |
E29404
|
entity |
| Predicate | ruralOrUrban |
P2460
|
FINISHED |
| Object | rural |
—
|
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: rural | Statement: [Mit Abu al-Kum, ruralOrUrban, rural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ruralOrUrban Context triple: [Mit Abu al-Kum, ruralOrUrban, rural]
-
A.
isRural
chosen
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
B.
isUrbanized
Indicates that a place or area has been developed with dense human settlement, infrastructure, and built environment characteristic of a city or town.
-
C.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
D.
containsUrbanArea
Indicates that a geographic region fully or partially encompasses an urbanized area within its boundaries.
-
E.
withinUrbanArea
Indicates that one entity is located inside the spatial boundaries of an urban area associated with 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c679714c8190ac53630fb49e19c5 |
completed | March 1, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69a4c486eacc81909c272f9bdf50a7c3 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:11 p.m.