Triple
T1915089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muni Metro at Embarcadero Station |
E39997
|
entity |
| Predicate | hasZoneType |
P6822
|
FINISHED |
| Object | urban core |
—
|
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: urban core | Statement: [Muni Metro at Embarcadero Station, hasZoneType, urban core]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasZoneType Context triple: [Muni Metro at Embarcadero Station, hasZoneType, urban core]
-
A.
hasZone
Indicates that one entity possesses, contains, or is associated with a specific zone or designated area.
-
B.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
C.
hasFareZone
Indicates that an entity is located within or associated with a specific fare zone used for pricing or ticketing.
-
D.
hasRailwayZone
Indicates that a location or railway entity falls under the jurisdiction or coverage area of a specific railway zone.
-
E.
hasSpaceType
Indicates that one entity is associated with, or classified by, a particular type or category of space.
- 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_69a8864298748190a2f2fd34f7ef8d77 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb34d94fc8190a5bf1e582c77c725 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafeba3d88190afcce67483d8625b |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.