Triple
T15964485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Short Round |
E387148
|
entity |
| Predicate | helpsRescue |
P80982
|
FINISHED |
| Object | enslaved children of Pankot Palace |
—
|
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: enslaved children of Pankot Palace | Statement: [Short Round, helpsRescue, enslaved children of Pankot Palace]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: helpsRescue Context triple: [Short Round, helpsRescue, enslaved children of Pankot Palace]
-
A.
rescuesFrom
Indicates that one entity saves or frees another entity from a dangerous, harmful, or undesirable situation or source.
-
B.
rescuesWith
chosen
Indicates that one entity saves or frees another entity from danger, harm, or captivity using a particular means, tool, or method.
-
C.
rescuesContext
Indicates that one entity saves or delivers another entity from danger, harm, or a problematic situation within a specific contextual setting.
-
D.
helpsSurvive
Indicates that one entity provides support or advantage that increases another entity’s chances of survival.
-
E.
numberOfRescuers
Indicates the quantity of rescuers involved in or assigned to a particular rescue-related situation or event.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.