Triple
T21593287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | J. S. Aurora |
E532832
|
entity |
| Predicate | numberOfPakistaniPrisonersTaken |
P144089
|
FINISHED |
| Object | about 93000 |
—
|
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: about 93000 | Statement: [J. S. Aurora, numberOfPakistaniPrisonersTaken, about 93000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPakistaniPrisonersTaken Context triple: [J. S. Aurora, numberOfPakistaniPrisonersTaken, about 93000]
-
A.
UnionPrisonersTaken
Indicates that Union forces captured and held individuals as prisoners.
-
B.
numberOfEscapedArabPrisoners
Indicates the count of Arab prisoners who have escaped from custody.
-
C.
numberOfEscapedIrgunAndLehiPrisoners
Indicates the count of prisoners affiliated with Irgun and Lehi who successfully escaped.
-
D.
prisonersTakenByOttomans
Indicates that the referenced individuals were captured and held as prisoners by Ottoman forces.
-
E.
captivesTaken
Indicates that one entity has taken another entity or group into captivity, holding them against their will.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefadf6e608190b42b26ea22c76ec6 |
completed | April 27, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e632109d048190b4ac3f14fe48d1a0 |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e63384a8fc819084c596bb53a3a1db |
completed | April 20, 2026, 2:09 p.m. |
Created at: April 16, 2026, 6:32 p.m.