Triple
T35720422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medieval World |
E1032454
|
entity |
| Predicate | safetyAssumptionInFiction |
P135926
|
FINISHED |
| Object | robots cannot harm guests |
—
|
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: robots cannot harm guests | Statement: [Medieval World, safetyAssumptionInFiction, robots cannot harm guests]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyAssumptionInFiction Context triple: [Medieval World, safetyAssumptionInFiction, robots cannot harm guests]
-
A.
safetyInFiction
chosen
Indicates that a work of fiction portrays conditions, measures, or themes related to safety, risk, or protection within its narrative world.
-
B.
hasRiskInFiction
Indicates that a subject is associated with a potential danger, threat, or harmful outcome within a fictional or narrative context.
-
C.
guardedByInFiction
Indicates that one fictional entity is protected or watched over by another within a narrative context.
-
D.
worksUnderInFiction
Indicates that, within a fictional context or narrative, one character or entity is hierarchically subordinate to and takes direction from another.
-
E.
worksInFictionalContext
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
- 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_69f76e102b5881909e5d63a30a5cecbe |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d219f8819081dc4ce3c83ca0cb |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:05 p.m.