Triple
T8571231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pacific Mall |
E202929
|
entity |
| Predicate | hasNumberOfUnitsApprox |
P19248
|
FINISHED |
| Object | over 400 retail 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: over 400 retail units | Statement: [Pacific Mall, hasNumberOfUnitsApprox, over 400 retail units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfUnitsApprox Context triple: [Pacific Mall, hasNumberOfUnitsApprox, over 400 retail units]
-
A.
numberOfUnits
chosen
Indicates the quantity or count of discrete units associated with an entity or relationship.
-
B.
hasDimensionsApprox
Indicates that an entity has physical dimensions that are known only approximately, rather than as exact measurements.
-
C.
hasUnitOf
Indicates that a quantity, measurement, or value is expressed in terms of a specific unit.
-
D.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
E.
usesQuantity
Indicates that one entity employs or applies a specified amount or measure of another entity in performing an action or fulfilling a function.
- 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_69ca8327b0a881908606ff860713964d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea4223888190a56d9026ae0b9ec0 |
completed | March 31, 2026, 3:37 p.m. |
| PD | Predicate disambiguation | batch_69cbd11856048190a1ce4b83a38f6965 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:21 p.m.