Triple
T37331508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stage Manager (Our Town) |
E926762
|
entity |
| Predicate | explainsSpatialShifts |
P187985
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Stage Manager (Our Town), explainsSpatialShifts, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: explainsSpatialShifts Context triple: [Stage Manager (Our Town), explainsSpatialShifts, true]
-
A.
explainsTemporalShifts
Indicates that one entity provides an account or clarification of changes or transitions occurring over time in another entity or process.
-
B.
spatialEffect
Indicates a spatial relationship where one entity affects or alters the position, arrangement, or spatial properties of another.
-
C.
shiftsWhen
Indicates that one state, condition, or configuration changes to another under specified circumstances or triggers.
-
D.
spatialCorrelation
Indicates a relationship where two spatial variables or patterns vary together in a statistically related way across space.
-
E.
shiftsThrough
Indicates that one entity moves or passes through another entity or medium, typically involving a change in position or state during the traversal.
- 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_69f76eb386d88190a8d511aa11540dfc |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb9e1845e881908d19158440cf3b87 |
completed | May 6, 2026, 8:01 p.m. |
| PD | Predicate disambiguation | batch_69fb8d08d6988190a00794ac26078348 |
completed | May 6, 2026, 6:48 p.m. |
| PDg | Predicate description generation | batch_69fb9e173f348190b7ab5935e4dca039 |
completed | May 6, 2026, 8:01 p.m. |
Created at: May 3, 2026, 4:16 p.m.