Triple
T7620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayflower landing at Plymouth |
E151
|
entity |
| Predicate | passengersCountApproximate |
P882
|
FINISHED |
| Object | 102 |
—
|
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: 102 | Statement: [Mayflower landing at Plymouth, passengersCountApproximate, 102]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengersCountApproximate Context triple: [Mayflower landing at Plymouth, passengersCountApproximate, 102]
-
A.
visitorCount
Indicates the number of visitors associated with a particular entity, context, or time period.
-
B.
collectionSize
Indicates the total number of items contained within a specified collection.
-
C.
isAbout
Indicates that one entity has as its subject, focus, or primary concern the content, topic, or theme represented by another entity.
-
D.
fareSystem
Indicates a relationship where a system is used to determine, collect, or manage fares or payments for transportation or similar services.
-
E.
near
Indicates that one entity is located at a short distance from another entity in space or position.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a241a55ac081909e95b71c97db8140 |
completed | Feb. 28, 2026, 1:15 a.m. |
| PD | Predicate disambiguation | batch_69a23fe1cf38819080ea56c40bf2632e |
completed | Feb. 28, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69a241a4a0f481908de66b64c6262fcd |
completed | Feb. 28, 2026, 1:15 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.