Triple
T17678117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 17/35 (El Tepual International Airport) |
E440689
|
entity |
| Predicate | length (feet) |
P118899
|
FINISHED |
| Object | 8632 |
—
|
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: 8632 | Statement: [Runway 17/35 (El Tepual International Airport), length (feet), 8632]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: length (feet) Context triple: [Runway 17/35 (El Tepual International Airport), length (feet), 8632]
-
A.
hasLengthFeet
chosen
Indicates that an entity possesses a length measured in feet.
-
B.
feetFeature
Indicates that one entity has a specific characteristic, attribute, or notable aspect related to its feet.
-
C.
heightFeet
Indicates a relationship where a subject has its vertical size or stature specified in feet as a unit of measurement.
-
D.
dimensionOfLength
Indicates that something represents or specifies a measurement along a single spatial extent (a length dimension).
-
E.
depthFeetApprox
Indicates an approximate measurement of an entity’s depth expressed in feet.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6e959c819092d9d33e79bd51f7 |
completed | April 19, 2026, 6 a.m. |
| PD | Predicate disambiguation | batch_69e3cde3673c8190a889e14ba1f07dc1 |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 10:01 a.m.