Triple
T29345779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corporal Peter Newkirk |
E744167
|
entity |
| Predicate | prisonCampNumber |
P166662
|
FINISHED |
| Object | Stalag 13 |
—
|
NE NERFINISHED |
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: Stalag 13 | Statement: [Corporal Peter Newkirk, prisonCampNumber, Stalag 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: prisonCampNumber Context triple: [Corporal Peter Newkirk, prisonCampNumber, Stalag 13]
-
A.
prisonerNumber
Indicates that an entity is assigned a specific identification number used to uniquely identify them as a prisoner.
-
B.
laterPrisonerNumber
Indicates that one prisoner has a higher (and thus later-assigned) prisoner identification number than another prisoner.
-
C.
prisonName
Indicates that an entity is known by a particular prison’s name.
-
D.
nicknameOfPrison
Indicates that one name is an informal or alternative nickname used to refer to a particular prison.
-
E.
estimatedPrisonerCount
Indicates the estimated number of prisoners associated with a particular context, such as a location, time period, or event.
- 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_69f0a79a2d748190bc30abd469298b37 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f66957a1f0819094c1be1055b97f47 |
completed | May 2, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69f660f4f7a88190b93c60d76b86c912 |
completed | May 2, 2026, 8:39 p.m. |
| PDg | Predicate description generation | batch_69f661b47d088190934f63884a203261 |
completed | May 2, 2026, 8:42 p.m. |
Created at: April 28, 2026, 2:02 p.m.