Triple
T12576005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MRT Blue Line |
E300207
|
entity |
| Predicate | extensionOpeningYear |
P105509
|
FINISHED |
| Object | 2019 |
—
|
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: 2019 | Statement: [MRT Blue Line, extensionOpeningYear, 2019]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: extensionOpeningYear Context triple: [MRT Blue Line, extensionOpeningYear, 2019]
-
A.
extensionOpeningPeriod
Indicates the time span during which an extension (such as a facility, service, or feature) is open or available for use.
-
B.
applicationYear
Indicates the year in which an application was submitted or made.
-
C.
campusOpeningYear
Indicates the calendar year in which a campus first officially opened or began operation.
-
D.
yearTaken
Indicates the specific calendar year in which an action, event, or record associated with the subject was carried out or occurred.
-
E.
matriculationYear
Indicates the calendar year in which an individual formally enrolled or was admitted into an educational program or institution.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9550d84908190aea0f50055f6d92e |
completed | April 10, 2026, 7:52 p.m. |
| PD | Predicate disambiguation | batch_69d95414692881909c52a1de7d224b44 |
completed | April 10, 2026, 7:48 p.m. |
| PDg | Predicate description generation | batch_69d9550af6d48190a40e349ed0424be3 |
completed | April 10, 2026, 7:52 p.m. |
Created at: April 9, 2026, 4:47 p.m.