Triple
T13103747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Galloping Gertie |
E310786
|
entity |
| Predicate | mainSpanLengthApprox |
P4063
|
FINISHED |
| Object | 853 meters |
—
|
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: 853 meters | Statement: [Galloping Gertie, mainSpanLengthApprox, 853 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainSpanLengthApprox Context triple: [Galloping Gertie, mainSpanLengthApprox, 853 meters]
-
A.
navigationSpanLength
Indicates the distance or extent covered by a navigation segment or span within a route or path.
-
B.
hasMainSpanLength
chosen
Indicates the relationship specifying the primary or main span’s length associated with an entity.
-
C.
largestSpan
Indicates that the referenced entity has the greatest extent or coverage (in distance, time, or range) among a set of comparable spans.
-
D.
approximateLengthInMeters
Indicates the estimated or roughly measured length of something expressed in meters.
-
E.
numberOfSpans
Indicates the total count of distinct spans or segments associated with an entity or within a specified context.
- 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98153255c8190b6ab64ac0c4716f8 |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d98041a3548190a05ddd83dbb660fa |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:04 p.m.