Triple
T8462394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 1 (Shanghai Metro) |
E200073
|
entity |
| Predicate | servesCentralBusinessDistrict |
P68209
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Line 1 (Shanghai Metro), servesCentralBusinessDistrict, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesCentralBusinessDistrict Context triple: [Line 1 (Shanghai Metro), servesCentralBusinessDistrict, true]
-
A.
servesDowntownArea
chosen
Indicates that an entity provides service or coverage to the downtown area.
-
B.
servesCentralLondon
Indicates that something provides service or access specifically to the Central London area.
-
C.
centralBusiness
Indicates that an entity functions as the primary commercial or economic hub (central business area or role) in relation to another entity.
-
D.
hasBusinessDistrict
Indicates that a place or administrative area contains or includes a designated business district within its boundaries.
-
E.
hasDowntownCharacteristic
Indicates that something possesses a feature, quality, or attribute typically associated with a downtown area.
- 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_69ca83198c4c8190a337bf717d1813f5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4a251f08190840a7fc31ff528b5 |
completed | March 31, 2026, 3:13 p.m. |
| PD | Predicate disambiguation | batch_69cbd0fc634481909842c0a30077bfde |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:10 p.m.