Triple
T31026815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ho Chi Minh City Metro |
E790599
|
entity |
| Predicate | line1NumberOfStations |
P171325
|
FINISHED |
| Object | over 10 |
—
|
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: over 10 | Statement: [Ho Chi Minh City Metro, line1NumberOfStations, over 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: line1NumberOfStations Context triple: [Ho Chi Minh City Metro, line1NumberOfStations, over 10]
-
A.
numberOfMetroLines
Indicates the total count of metro (subway) lines associated with or serving a given entity.
-
B.
numberOfStations
Indicates the total count of stations associated with or contained by a given entity.
-
C.
numberOfRailLines
Indicates the total count of rail lines associated with or serving a given entity.
-
D.
numberOfStationsOpenedInPhase1
Indicates the total count of stations that were opened during the first phase of a project or rollout.
-
E.
numberOfUndergroundStations
Indicates the total count of underground (subway/metro) stations associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69f224c811508190a7de096a5b1f5798 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69f80b62c8190bf2af2be0d3a7df8 |
completed | May 3, 2026, 1:06 a.m. |
| PD | Predicate disambiguation | batch_69f69d1a37e081908d1d86b90ff502bd |
completed | May 3, 2026, 12:55 a.m. |
| PDg | Predicate description generation | batch_69f69dfbf6ac8190ba2e6fc0adfd8b73 |
completed | May 3, 2026, 12:59 a.m. |
Created at: April 29, 2026, 8:58 p.m.