Triple
T18127613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dow Theory |
E433917
|
entity |
| Predicate | primaryTrendTypicalDuration |
P84192
|
FINISHED |
| Object | more than one year |
—
|
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: more than one year | Statement: [Dow Theory, primaryTrendTypicalDuration, more than one year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryTrendTypicalDuration Context triple: [Dow Theory, primaryTrendTypicalDuration, more than one year]
-
A.
typicalPeriod
chosen
Indicates the usual or characteristic time interval or duration associated with an event, process, or state.
-
B.
typicalDurationDays
Indicates the usual or expected number of days that an associated event, process, or state typically lasts.
-
C.
closureTrend
Indicates how the status or frequency of closures changes over time (e.g., increasing, decreasing, or stable).
-
D.
reversedTrendOf
Indicates that one trend is the inverse or opposite-direction counterpart of another trend, such that when one increases or moves in a certain direction, the other correspondingly decreases or moves in the opposite direction.
-
E.
typicalTimes
Indicates the usual or characteristic times at which an event, activity, or condition typically occurs.
- 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_69d8b909e8cc81908df4cc2b8ea6d11f |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddef4cd88190b16ef0d6ed3968c6 |
completed | April 19, 2026, 1:51 p.m. |
| PD | Predicate disambiguation | batch_69e43313ca788190baa224269e71de49 |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:29 a.m.