Triple
T25997858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M1 line |
E646533
|
entity |
| Predicate | connectsCentralDistrictsWith |
P177095
|
FINISHED |
| Object | airport |
—
|
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: airport | Statement: [M1 line, connectsCentralDistrictsWith, airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsCentralDistrictsWith Context triple: [M1 line, connectsCentralDistrictsWith, airport]
-
A.
connectsCentralAreaTo
Indicates a relationship where one element serves as a link or pathway between a central area and another location or component.
-
B.
connectsCityTo
Indicates a relationship in which a route, infrastructure, or link joins one city to another, enabling connection or interaction between them.
-
C.
connectsDowntownTo
Indicates a relationship where one location, route, or service provides a direct connection or access to a downtown area.
-
D.
connectsKeyDistrict
Indicates that one entity establishes or maintains a significant linkage or route to a strategically important or central district.
-
E.
connectsMunicipalities
Indicates a relationship where one entity serves as a link or route that joins two or more municipalities.
- 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_69e77e88cb8481908da31d4a00661f55 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6f85bfba48190aba95b40642a8ca7 |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f65fd1d08190b88e5e68ba268500 |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f854486c81909396d944a55e03ab |
completed | May 3, 2026, 7:25 a.m. |
Created at: April 22, 2026, 8:58 a.m.