Triple
T36352830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Linea, San Francisco |
E895253
|
entity |
| Predicate | hasUnits |
P193910
|
FINISHED |
| Object | residential units |
—
|
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: residential units | Statement: [The Linea, San Francisco, hasUnits, residential units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUnits Context triple: [The Linea, San Francisco, hasUnits, residential units]
-
A.
hasUnitOf
Indicates that a quantity, measurement, or value is expressed in terms of a specific unit.
-
B.
hasUnitIn
Indicates that one entity is contained or measured within another as a unit, specifying a unit-of-measure or component relationship.
-
C.
hasUnitsFrom
Indicates that one entity derives, adopts, or uses its measurement units from another specified source entity.
-
D.
hasBasedUnits
Indicates that a derived measurement unit is defined in terms of one or more underlying base units.
-
E.
hasUnitConstant
Indicates that something is associated with a fixed, standard unit of measurement used to express its value.
- 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_69f76e4f437c8190a1af3ea2564f41f5 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd592e48cc81909d754cc6c4bd99ae |
completed | May 8, 2026, 3:31 a.m. |
| PD | Predicate disambiguation | batch_69fd58b7f9b881909dc099b28d567784 |
completed | May 8, 2026, 3:30 a.m. |
| PDg | Predicate description generation | batch_69fd592cc56081908ce456114d407616 |
completed | May 8, 2026, 3:31 a.m. |
Created at: May 3, 2026, 4:09 p.m.