Triple
T4845063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LEED v4 |
E108267
|
entity |
| Predicate | includesCreditCategory |
P59738
|
FINISHED |
| Object | Location and Transportation |
—
|
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: Location and Transportation | Statement: [LEED v4, includesCreditCategory, Location and Transportation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesCreditCategory Context triple: [LEED v4, includesCreditCategory, Location and Transportation]
-
A.
supportsSpendingCategory
Indicates that one entity allows, enables, or is compatible with making expenditures in a specified spending category.
-
B.
hasCredit
Indicates that an entity possesses or is assigned a credit, such as financial credit, academic credit, or acknowledgment for a contribution.
-
C.
requiresCreditAs
Indicates that one entity must be credited or acknowledged in a specified manner or role in relation to another entity.
-
D.
eligibilityCategory
Indicates the classification or type of eligibility that applies to an entity within a given context.
-
E.
grantsCreditFor
Indicates that one entity recognizes or awards academic or other formal credit to another entity for a specific activity, course, or achievement.
- 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_69bd4409b264819085ab855f3eb5381a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e01872c81909607010c10538ad1 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2375a4819098e16acb982c8fab |
completed | March 20, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69bd6dfff1488190a32bbb615bfab970 |
completed | March 20, 2026, 3:55 p.m. |
Created at: March 20, 2026, 1:25 p.m.