Triple
T21612127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legazpi |
E533337
|
entity |
| Predicate | metroZone |
P99181
|
FINISHED |
| Object | A |
—
|
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: A | Statement: [Legazpi, metroZone, A]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: metroZone Context triple: [Legazpi, metroZone, A]
-
A.
metro
Indicates a relationship where an entity is associated with, located in, or served by a metropolitan transit system (such as a subway or urban rail network).
-
B.
metropolitan
Indicates that a location is part of, belongs to, or lies within a specified metropolitan (urban) area.
-
C.
metropole
Indicates a relationship where one place functions as the principal or most important city or center of activity for another place or region.
-
D.
zonedTo
Indicates that one entity is assigned or designated to fall within the jurisdiction, service area, or regulatory zone of another entity.
-
E.
fareZoneDescription
chosen
Indicates the textual description of the fare zone associated with a service, location, or segment.
- 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_69e0c46411108190bba0d4176dffc9f3 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef3ba79424819094e9ee93c4bbcc0b |
completed | April 27, 2026, 10:34 a.m. |
| PD | Predicate disambiguation | batch_69e69665fe8c8190af7e38785db188b2 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:33 p.m.