Triple
T28186872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ZIPPY |
E716203
|
entity |
| Predicate | baseMetropolitanArea |
P42426
|
FINISHED |
| Object | Greater Tokyo Area |
—
|
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: Greater Tokyo Area | Statement: [ZIPPY, baseMetropolitanArea, Greater Tokyo Area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: baseMetropolitanArea Context triple: [ZIPPY, baseMetropolitanArea, Greater Tokyo Area]
-
A.
metropolitanAreaWith
chosen
Indicates that one entity is a metropolitan area that includes, is associated with, or encompasses the other entity.
-
B.
metropolitanAreaType
Indicates the classification of a metropolitan area according to its type or category (e.g., size, function, or administrative status).
-
C.
largestMetropolitanArea
Indicates that one entity is the largest metropolitan area associated with, contained within, or relevant to another entity, typically by population or spatial extent.
-
D.
representsMetropolitanArea
Indicates that one entity serves as or corresponds to the metropolitan area associated with another entity.
-
E.
metropolitanOf
Indicates that one place serves as the primary metropolitan center or core urban area for another place or region.
- 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_69efd6b4fc5c81909dd88f01a8c2b35d |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f64286c11c81909ee026bd8fbc2baf |
completed | May 2, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69f63c6c1a948190b68c0f92c264cc0c |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 10:23 p.m.