Triple
T32683578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | APM line (Guangzhou Metro) |
E835654
|
entity |
| Predicate | hasPeakHeadway |
P190632
|
FINISHED |
| Object | approximately 2 minutes |
—
|
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: approximately 2 minutes | Statement: [APM line (Guangzhou Metro), hasPeakHeadway, approximately 2 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPeakHeadway Context triple: [APM line (Guangzhou Metro), hasPeakHeadway, approximately 2 minutes]
-
A.
peakServiceHeadway
chosen
Indicates the typical maximum time interval between consecutive services during peak operating periods.
-
B.
hasPeak
Indicates that something possesses or contains a highest point, summit, or maximum value.
-
C.
hasPeakHourFrequency
Indicates how often a service or event occurs during designated peak hours.
-
D.
hasPeakHourService
Indicates that a service operates or is available during designated peak or high-demand hours.
-
E.
hasPeakHourFunction
Indicates that something performs a specific role or behavior during peak hours of activity or usage.
- 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_69f3493211388190993801216afbc2a7 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fd6dbd1b648190b1a0b391c03aebc5 |
completed | May 8, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69fd6a9020548190bbfa845360ac85fb |
completed | May 8, 2026, 4:46 a.m. |
Created at: May 1, 2026, 1:09 a.m.