Triple
T1995337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hangar One |
E43344
|
entity |
| Predicate | visualLandmarkFor |
P13974
|
FINISHED |
| Object | air and ground travelers in Silicon Valley |
—
|
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: air and ground travelers in Silicon Valley | Statement: [Hangar One, visualLandmarkFor, air and ground travelers in Silicon Valley]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualLandmarkFor Context triple: [Hangar One, visualLandmarkFor, air and ground travelers in Silicon Valley]
-
A.
visualFeature
Indicates a relationship where one entity possesses or exhibits a particular visual characteristic or attribute of another entity.
-
B.
includesLandmark
Indicates that one location or area contains or encompasses a specific landmark within its boundaries.
-
C.
locationOfVision
Indicates the place or setting where a vision or visual experience occurs or is perceived.
-
D.
isLocalLandmark
chosen
Indicates that something is recognized as a notable or significant landmark within a specific local area or community.
-
E.
featureOfInterest
Indicates the entity or object that is the primary subject or focus of the described observation, measurement, or analysis.
- 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_69a88714cf2c819081644be450b8356e |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb8ee02dc81908fec9fd8df7a4f40 |
completed | March 7, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69abb79ad6888190be99943a9c73cf3e |
completed | March 7, 2026, 5:28 a.m. |
Created at: March 4, 2026, 7:37 p.m.