Triple
T1242692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kent mango |
E26692
|
entity |
| Predicate | transportTolerance |
P25914
|
FINISHED |
| Object | good |
—
|
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: good | Statement: [Kent mango, transportTolerance, good]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transportTolerance Context triple: [Kent mango, transportTolerance, good]
-
A.
transportReliability
Indicates how consistently and dependably a transport service or system performs as expected over time.
-
B.
transportFor
Indicates a relationship where one entity serves as the means or service used to move another entity from one place to another.
-
C.
hasTransportRoute
Indicates that there exists a designated transportation connection or route linking one entity to another.
-
D.
transportType
Indicates the mode or means of transportation used in carrying something or someone from one place to another.
-
E.
travelTimeCategory
Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf61fadc8190b7b9e23eaa15a61d |
completed | March 1, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69a4bb696a38819095845c84f0241287 |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bce611ec819092cb13d354d0903e |
completed | March 1, 2026, 10:25 p.m. |
Created at: March 1, 2026, 7:47 p.m.