Triple
T38051299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Recto Avenue |
E949766
|
entity |
| Predicate | hasNearbyDistrictNickname |
P130276
|
FINISHED |
| Object | University Belt |
—
|
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: University Belt | Statement: [Recto Avenue, hasNearbyDistrictNickname, University Belt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyDistrictNickname Context triple: [Recto Avenue, hasNearbyDistrictNickname, University Belt]
-
A.
nicknameOfNeighborhoodItMarks
chosen
Indicates that something serves as a marker or reference for a neighborhood by bearing or using that neighborhood’s nickname.
-
B.
hasNearbyCityArea
Indicates that one area is geographically close to or adjacent to a city area.
-
C.
locatedNearDistrict
Indicates that one entity is situated in close geographic proximity to a particular district.
-
D.
hasNearbyGeographicalArea
Indicates that one geographical area is located in close spatial proximity to another geographical area.
-
E.
hasNearbyAdministrativeUnit
Indicates that one administrative unit is geographically close to another administrative unit.
- 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_69f76f000cf081908c11fb5443b392e6 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a00b8e0a5508190abc5c1e492bed12e |
completed | May 10, 2026, 4:57 p.m. |
| PD | Predicate disambiguation | batch_6a00b8327d048190850af317f60f0f8b |
completed | May 10, 2026, 4:54 p.m. |
Created at: May 3, 2026, 4:20 p.m.