Triple
T32746385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orbital Maneuvering System |
E837363
|
entity |
| Predicate | typicalBurnDurationRange |
P43931
|
FINISHED |
| Object | tens to hundreds of seconds |
—
|
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: tens to hundreds of seconds | Statement: [Orbital Maneuvering System, typicalBurnDurationRange, tens to hundreds of seconds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBurnDurationRange Context triple: [Orbital Maneuvering System, typicalBurnDurationRange, tens to hundreds of seconds]
-
A.
typicalDurationMinutes
Indicates the usual or expected length of time, measured in minutes, that an event, activity, or process typically lasts.
-
B.
estimatedBurnDuration
chosen
Indicates the expected length of time that something is predicted to burn or remain burning.
-
C.
typicalDurationDays
Indicates the usual or expected number of days that an associated event, process, or state typically lasts.
-
D.
banDurationApproximate
Indicates that the duration of a ban is known only approximately rather than as an exact, precise time period.
-
E.
typicalBoutLength
Indicates the usual or characteristic duration of a single bout or episode of an activity or behavior.
- 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_69f34936e1748190b797e406e4e9293a |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a0379f0cbe481909b4b8fc6cbe297f0 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:12 a.m.