Triple
T29442706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belait District |
E746757
|
entity |
| Predicate | hasNumberOfMukims |
P192707
|
FINISHED |
| Object | several mukims |
—
|
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: several mukims | Statement: [Belait District, hasNumberOfMukims, several mukims]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfMukims Context triple: [Belait District, hasNumberOfMukims, several mukims]
-
A.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
B.
hasNumberOfSubdistricts
Indicates the relationship specifying how many subdistricts are associated with a given entity.
-
C.
hasNumberOfBarangays
Indicates the total count of barangays associated with a given administrative unit or locality.
-
D.
hasNumberOfUnionCouncils
Indicates the relationship specifying how many union councils are associated with or contained within a given entity.
-
E.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69f0a7a180e48190ae775e40047dbcb5 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69fd2839880c819099a7a89783f2270e |
completed | May 8, 2026, 12:03 a.m. |
| PD | Predicate disambiguation | batch_69fd23dc5da48190ae8ba08947d34956 |
completed | May 7, 2026, 11:44 p.m. |
| PDg | Predicate description generation | batch_69fd28379d2c8190903ba228ee1cc756 |
completed | May 8, 2026, 12:03 a.m. |
Created at: April 28, 2026, 3:24 p.m.