Triple
T8388749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tegel Prison |
E197886
|
entity |
| Predicate | numberOfInmates |
P14018
|
FINISHED |
| Object | over 1000 |
—
|
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 1000 | Statement: [Tegel Prison, numberOfInmates, over 1000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInmates Context triple: [Tegel Prison, numberOfInmates, over 1000]
-
A.
numberOfPrisonersApproximate
Indicates an approximate count of prisoners associated with an entity or situation, rather than an exact number.
-
B.
estimatedPrisonerCount
chosen
Indicates the estimated number of prisoners associated with a particular context, such as a location, time period, or event.
-
C.
inmates
Indicates that one entity is confined or held as a prisoner within an institution or facility associated with another entity.
-
D.
lastRemainingInmate
Indicates that the subject is the final inmate still present or remaining in a given context, after all others are gone.
-
E.
numberOfPrisonSentences
Indicates the count of distinct prison sentences that have been imposed on a given individual or 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_69ca82f749388190bffbea6dfb509016 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb81090f688190a3a8d1680383c361 |
completed | March 31, 2026, 8:08 a.m. |
| PD | Predicate disambiguation | batch_69cb70cfe82881909fe374ba52649e84 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:03 p.m.