Triple
T2792027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Focsa Building |
E61950
|
entity |
| Predicate | hasCommercialUnits |
P33790
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Focsa Building, hasCommercialUnits, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommercialUnits Context triple: [Focsa Building, hasCommercialUnits, yes]
-
A.
hasCommercialFunction
Indicates that an entity serves a commercial role or purpose, such as engaging in trade, sales, or other profit-oriented activities.
-
B.
hasRetailUnits
chosen
Indicates that one entity possesses, operates, or is associated with one or more retail units (such as stores or outlets).
-
C.
commercializedIn
Indicates that something has been brought to market or made available for commercial sale or use within a specified place or context.
-
D.
hasUnitStatus
Indicates that an entity is associated with a particular operational or condition status as a unit.
-
E.
hasUnitOf
Indicates that a quantity, measurement, or value is expressed in terms of a specific unit.
- 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_69ab4b7f51d881908768300ebd2fbdae |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdeea881481908d759c72798a50fb |
completed | March 7, 2026, 8:16 a.m. |
| PD | Predicate disambiguation | batch_69abdd025c948190a97dd961a9592bac |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:58 p.m.