Triple
T30429487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lotha |
E774119
|
entity |
| Predicate | demographicStatusInWokha |
P94260
|
FINISHED |
| Object | majority community |
—
|
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: majority community | Statement: [Lotha, demographicStatusInWokha, majority community]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: demographicStatusInWokha Context triple: [Lotha, demographicStatusInWokha, majority community]
-
A.
demographicStatusInChad
Indicates the demographic status or characteristics of an entity within the context of Chad.
-
B.
demographicScope
Indicates the specific population group or demographic segment to which something (e.g., a policy, study, product, or service) is targeted or applicable.
-
C.
demographicsIn
chosen
Indicates that demographic information is associated with or applies within a specified geographic or organizational area.
-
D.
demographicBasis
Indicates that something is determined, classified, or justified based on demographic characteristics such as age, gender, ethnicity, or similar population attributes.
-
E.
demographics
Indicates the relationship of providing or characterizing statistical information about a population’s attributes, such as age, gender, income, or education.
- 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_69f22492d2a88190995ce8745d9becaa |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6866c549c8190985ef4bf8032209e |
completed | May 2, 2026, 11:19 p.m. |
| PD | Predicate disambiguation | batch_69f678d2196c8190b9d0d2fcd47cc539 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 8:06 p.m.