Triple
T26971642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microcosm of London |
E679331
|
entity |
| Predicate | hasApproximateNumberOfPlates |
P33015
|
FINISHED |
| Object | 100 |
—
|
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: 100 | Statement: [Microcosm of London, hasApproximateNumberOfPlates, 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateNumberOfPlates Context triple: [Microcosm of London, hasApproximateNumberOfPlates, 100]
-
A.
numberOfPlates
chosen
Indicates the quantity of plates associated with or involved in a particular entity, event, or context.
-
B.
plateNumberOf
Indicates the license plate number that is assigned to or associated with a particular vehicle.
-
C.
hasPlate
Indicates that one entity possesses, is equipped with, or includes a plate as part of its attributes or components.
-
D.
hasApproximateNumberOfVarieties
Indicates that an entity is associated with an estimated or non-exact count of different varieties or types.
-
E.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
- 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_69eeeb507a7081909d516e1fa08b7d29 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6d6a6b04c8190bee4cf9c00665ef7 |
completed | May 3, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69f6d26ceb08819091c71c001e954936 |
completed | May 3, 2026, 4:43 a.m. |
Created at: April 27, 2026, 6:39 a.m.