Triple
T11185436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MP 44 |
E264652
|
entity |
| Predicate | estimatedNumberBuilt |
P2091
|
FINISHED |
| Object | over 400,000 units |
—
|
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: over 400,000 units | Statement: [MP 44, estimatedNumberBuilt, over 400,000 units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedNumberBuilt Context triple: [MP 44, estimatedNumberBuilt, over 400,000 units]
-
A.
numberBuilt
chosen
Indicates the total count of items or structures that have been constructed or produced.
-
B.
massProduced
Indicates that an item is manufactured in large quantities, typically using standardized, industrial production processes.
-
C.
builtOn
Indicates that one entity is constructed, developed, or established using another entity as its base, foundation, or underlying platform.
-
D.
builtInThe
Indicates that something was constructed or created within a specified location, structure, or context.
-
E.
numberOfBuildings
Indicates the total count of buildings associated with a given entity or within a specified context.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8abbeac8190ad6e419258999f4e |
completed | April 9, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69d75cf4461c8190af84060f7db83211 |
completed | April 9, 2026, 8:01 a.m. |
Created at: April 8, 2026, 9:29 p.m.