Triple
T38179216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Local Government Area of Nigeria |
E1005105
|
entity |
| Predicate | serviceSector |
P176113
|
FINISHED |
| Object | environmental sanitation |
—
|
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: environmental sanitation | Statement: [Local Government Area of Nigeria, serviceSector, environmental sanitation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serviceSector Context triple: [Local Government Area of Nigeria, serviceSector, environmental sanitation]
-
A.
serviceSectorPresence
Indicates the extent to which service-oriented economic activities are present or represented in a given context.
-
B.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
C.
economicSectors
Indicates a relationship that associates entities with the economic sectors or industries in which they operate or to which they belong.
-
D.
sectoralClassification
Indicates how an entity is categorized into a specific economic or industry sector within a classification scheme.
-
E.
sectoralExample
chosen
Indicates that something serves as a representative or illustrative example within a particular sector or industry context.
- 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_69f76dbc22c481908139b694ffde7a0c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fdb04ed81c8190b8feea90c1c785a6 |
completed | May 8, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69fda9d6c5148190a63205b6d9b0a1b4 |
completed | May 8, 2026, 9:16 a.m. |
Created at: May 3, 2026, 4:29 p.m.