Triple
T2955198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villa Alemana |
E79915
|
entity |
| Predicate | isCommuterTown |
P30159
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Villa Alemana, isCommuterTown, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isCommuterTown Context triple: [Villa Alemana, isCommuterTown, true]
-
A.
isCommuterRegionFor
Indicates that one region primarily serves as a residential base whose inhabitants regularly travel to another region for work or daily activities.
-
B.
isSuburbanCommunity
chosen
Indicates that a community is located in a suburban area, typically characterized by residential neighborhoods situated between urban centers and rural regions.
-
C.
isSuburbanCommunityIn
Indicates that a suburban community is located within or belongs to a specified larger geographic or administrative area.
-
D.
hasCommuterTraffic
Indicates that there is regular, recurring traffic flow associated with people traveling between their homes and places of work or study.
-
E.
isCompanyTownFor
Indicates that one location functions as a company town whose economy, services, or governance are predominantly controlled or dominated by a specific company.
- 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_69ad8b1276588190a374a0b12e0f7bdf |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99286ac8819084f02fbb0a1616d3 |
completed | March 8, 2026, 3:43 p.m. |
| PD | Predicate disambiguation | batch_69ad960c5c8881909d679912bd7d78f3 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:57 p.m.