Triple
T12309117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | P. Tuazon Boulevard |
E293429
|
entity |
| Predicate | hasCommercialEstablishmentsAlong |
P50976
|
FINISHED |
| Object | shopping centers |
—
|
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: shopping centers | Statement: [P. Tuazon Boulevard, hasCommercialEstablishmentsAlong, shopping centers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommercialEstablishmentsAlong Context triple: [P. Tuazon Boulevard, hasCommercialEstablishmentsAlong, shopping centers]
-
A.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars associated with a given entity.
-
B.
isCommercialFacility
Indicates that a facility is used primarily for commercial or business-related activities or services.
-
C.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
-
D.
hasRestaurantsAndBars
Indicates that the subject location contains or provides access to both restaurants and bars.
-
E.
hasCommercialInfrastructure
chosen
Indicates that an entity possesses or is equipped with facilities, systems, or structures that support commercial or business activities.
- 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f621570819091ee1db2609233ea |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ec02c008190a56aae60a3d9eff6 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:53 p.m.