Triple
T16155389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colorado Boulevard |
E392026
|
entity |
| Predicate | hasBusinessDistrictAlong |
P4285
|
FINISHED |
| Object | Old Pasadena shopping and dining district |
—
|
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: Old Pasadena shopping and dining district | Statement: [Colorado Boulevard, hasBusinessDistrictAlong, Old Pasadena shopping and dining district]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBusinessDistrictAlong Context triple: [Colorado Boulevard, hasBusinessDistrictAlong, Old Pasadena shopping and dining district]
-
A.
hasBusinessDistrict
Indicates that a place or administrative area contains or includes a designated business district within its boundaries.
-
B.
isInBusinessDistrict
Indicates that an entity is located within a designated business or commercial district area.
-
C.
hasShoppingDistrictName
Indicates that an entity’s shopping district is identified by a specific name.
-
D.
hasFinancialDistrict
Indicates that a place or city contains a designated area primarily devoted to financial and banking activities.
-
E.
hasShoppingDistrict
chosen
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21e5902a08190ad8694955ef6073a |
completed | April 17, 2026, 11:49 a.m. |
| PD | Predicate disambiguation | batch_69e1828abb608190a99d86bce1d77de2 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 5:01 a.m.