Triple
T24469719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DU |
E617064
|
entity |
| Predicate | refersToInstitutionCity |
P263
|
FINISHED |
| Object | Denver |
—
|
NE NERFINISHED |
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: Denver | Statement: [DU, refersToInstitutionCity, Denver]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToInstitutionCity Context triple: [DU, refersToInstitutionCity, Denver]
-
A.
cityOfInstitution
chosen
Indicates the city in which an institution is located or based.
-
B.
containsUniversityCity
Indicates that a given region or area includes within its boundaries a city that hosts a university.
-
C.
refersToCityAs
Indicates that one entity designates, mentions, or identifies another entity specifically as a city.
-
D.
refersToCityOn
Indicates that one entity makes reference to, or is specifically about, a particular city located on a given date or temporal context.
-
E.
isStudentCity
Indicates that a city is characterized by or primarily associated with a significant student population, such as hosting major educational institutions.
- 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_69e2d7f197588190889a03e620558059 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f299422cdc8190bc8d56243b7bc313 |
completed | April 29, 2026, 11:50 p.m. |
| PD | Predicate disambiguation | batch_69f287d76c7c81909494f12e606a9149 |
completed | April 29, 2026, 10:36 p.m. |
Created at: April 18, 2026, 2:20 a.m.