Triple
T4112105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mutianyu section |
E90199
|
entity |
| Predicate | relativeCrowdLevel |
P25304
|
FINISHED |
| Object | less crowded than Badaling section |
—
|
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: less crowded than Badaling section | Statement: [Mutianyu section, relativeCrowdLevel, less crowded than Badaling section]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeCrowdLevel Context triple: [Mutianyu section, relativeCrowdLevel, less crowded than Badaling section]
-
A.
hasCrowdLevel
Indicates the degree or intensity of how crowded a place, event, or situation is.
-
B.
crowdWas
Indicates that a crowd possessed or exhibited a particular state, quality, or condition.
-
C.
relativeLevel
chosen
Indicates a comparative relationship specifying how one entity’s level, degree, or intensity of some property stands relative to that of another entity.
-
D.
swarmDensity
Indicates the concentration or number of individuals within a swarm relative to a given area or volume.
-
E.
guestCountApproximate
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
- 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_69aed95c080881908125e30c5dcdc6f8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af03d7240c8190a64dcbc669772808 |
completed | March 9, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69af0183eb84819087d7184de28f5514 |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:41 p.m.