Triple
T36565098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Launch Complex 37 |
E901954
|
entity |
| Predicate | hasMobileServiceTower |
P146794
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Launch Complex 37, hasMobileServiceTower, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMobileServiceTower Context triple: [Launch Complex 37, hasMobileServiceTower, yes]
-
A.
hasServiceTower
chosen
Indicates that an entity is equipped with or associated with a service tower that supports its operation or maintenance.
-
B.
hasCellService
Indicates that a location, device, or area is within range of a cellular network and can access mobile phone or data services.
-
C.
hasCommunicationTowers
Indicates that one entity possesses, contains, or is equipped with communication towers used for transmitting or receiving signals.
-
D.
hasCellularComponent
Indicates that an entity possesses, includes, or is associated with a specific cellular component as part of its structure or organization.
-
E.
hasCellularModel
Indicates that one entity serves as a cellular (cell-based) model or system used to study, represent, or simulate the biological properties or behavior of another entity.
- 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_69f76e6416708190a9754b8c52d4e453 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ff64b957bc81908afbc5914234a8ea |
completed | May 9, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69ff6446593c81909173e296eea2590c |
completed | May 9, 2026, 4:43 p.m. |
Created at: May 3, 2026, 4:11 p.m.