Triple
T11867847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sicao Green Tunnel |
E282329
|
entity |
| Predicate | bestTimeOfDayToVisit |
P101963
|
FINISHED |
| Object | morning |
—
|
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: morning | Statement: [Sicao Green Tunnel, bestTimeOfDayToVisit, morning]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestTimeOfDayToVisit Context triple: [Sicao Green Tunnel, bestTimeOfDayToVisit, morning]
-
A.
popularTimeToVisit
Indicates the time period during which a place is most frequently visited or experiences peak visitor activity.
-
B.
bestTimeForPhotography
Indicates the most suitable or optimal time period for taking photographs, typically based on lighting or environmental conditions.
-
C.
peakHours
Indicates that an action, event, or condition occurs during the busiest or most heavily trafficked time period.
-
D.
landingTimeOfDay
Indicates the time of day at which a landing event occurs.
-
E.
activityPeakPeriod
Indicates the time period during which an activity reaches its highest level or intensity.
- 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a73a233081909449ab294d01a512 |
completed | April 10, 2026, 7:31 a.m. |
| PD | Predicate disambiguation | batch_69d8a2589f0c8190ad82ff11acabae93 |
completed | April 10, 2026, 7:10 a.m. |
| PDg | Predicate description generation | batch_69d8a43cc0c881909fed7cd759fe90b1 |
completed | April 10, 2026, 7:18 a.m. |
Created at: April 8, 2026, 9:43 p.m.