Triple
T7513074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidio Modelo |
E177569
|
entity |
| Predicate | numberOfCellBlocks |
P77399
|
FINISHED |
| Object | multiple circular blocks |
—
|
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: multiple circular blocks | Statement: [Presidio Modelo, numberOfCellBlocks, multiple circular blocks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCellBlocks Context triple: [Presidio Modelo, numberOfCellBlocks, multiple circular blocks]
-
A.
numberOfCells
Indicates the total count of individual cells associated with or contained in a given entity.
-
B.
estimatedNumberOfBlocks
Indicates the approximate count of discrete blocks associated with or involved in the given entity or context.
-
C.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
D.
numberOfZones
Indicates the quantity of distinct zones associated with or contained by a given entity.
-
E.
numberOfColumns
Indicates the total count of vertical divisions (columns) associated with or contained in a given structure or dataset.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5d52b2c8190ba32b1575756fa7c |
completed | March 27, 2026, 9:25 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d44e9481909813e073b194f6f4 |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6f574d8a8819095749518dad13791 |
completed | March 27, 2026, 9:24 p.m. |
Created at: March 27, 2026, 3:45 p.m.