Triple
T32306691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prome District (historical) |
E825389
|
entity |
| Predicate | containedTownship |
P22464
|
FINISHED |
| Object |
Prome Township
Prome Township is an administrative township centered on the historic city of Pyay (formerly Prome) in the Bago Region of Myanmar.
|
E2001703
|
NE FINISHED |
How this triple was built (3 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: Prome Township | Statement: [Prome District (historical), containedTownship, Prome Township]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Prome Township Triple: [Prome District (historical), containedTownship, Prome Township]
Generated description
Prome Township is an administrative township centered on the historic city of Pyay (formerly Prome) in the Bago Region of Myanmar.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containedTownship Context triple: [Prome District (historical), containedTownship, Prome Township]
-
A.
hasTownship
chosen
Indicates that one administrative area or jurisdiction includes or is associated with a specific township.
-
B.
regionContainsTowns
Indicates that a geographic region includes or encompasses one or more towns within its boundaries.
-
C.
containsSuburbanAreaOf
Indicates that one geographic region includes within its boundaries a suburban area belonging to or associated with another region.
-
D.
isInteriorTownOf
Indicates that one town is located within the interior region of, and is administratively or geographically associated with, a larger area or jurisdiction.
-
E.
involvedTown
Indicates that a town participates in, is associated with, or is affected by a particular event, activity, or relationship.
- F. None of above.
Provenance (6 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_69f349115304819084ee91d345b6c8aa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ffc1550cb481908628e446d9b67f7b |
completed | May 9, 2026, 11:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a305712a02c81908eaa9aad47fe3236 |
completed | June 15, 2026, 7:48 p.m. |
| NEDg | Description generation | batch_6a305ad097f481908935fa484b3aba59 |
completed | June 15, 2026, 8:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a305b7872348190aaa5fd5c7eb1a955 |
completed | June 15, 2026, 8:07 p.m. |
| PD | Predicate disambiguation | batch_69ffc10a74708190ae90e2c378791f70 |
completed | May 9, 2026, 11:19 p.m. |
Created at: May 1, 2026, 12:45 a.m.