Triple
T9739154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University Street |
E236141
|
entity |
| Predicate | nameImplies |
P34008
|
FINISHED |
| Object | educational character of surrounding area |
—
|
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: educational character of surrounding area | Statement: [University Street, nameImplies, educational character of surrounding area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nameImplies Context triple: [University Street, nameImplies, educational character of surrounding area]
-
A.
nameIndicates
chosen
Indicates that one entity’s name signifies, reveals, or is used to denote a particular property, identity, or characteristic of another entity.
-
B.
nameLiterallyMeans
Indicates that the literal meaning or direct translation of one entity’s name is given by the other entity.
-
C.
nameOf
Indicates that one entity is the name or designation of another entity.
-
D.
namedIn
Indicates that one entity is explicitly mentioned or referenced by name within another entity (such as a document, statement, or record).
-
E.
namesakeDescription
Indicates that the object provides a descriptive explanation of why or how the subject is considered a namesake of something or someone.
- 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_69ca84d313e88190983ee6ffd0ef60d2 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9ef43fec8190987628f401a27436 |
completed | April 1, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:22 p.m.