Triple
T1491073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Someone Comes to Town, Someone Leaves Town |
E29579
|
entity |
| Predicate | hasFreeLicenseEdition |
P9402
|
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: [Someone Comes to Town, Someone Leaves Town, hasFreeLicenseEdition, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFreeLicenseEdition Context triple: [Someone Comes to Town, Someone Leaves Town, hasFreeLicenseEdition, yes]
-
A.
freeEdition
chosen
Indicates that something is provided as a version that can be used without payment or licensing fees.
-
B.
hasLicense
Indicates that an entity possesses a valid authorization or permit, typically granted by an authority, to perform a specific activity or use something.
-
C.
supportsLicense
Indicates that one entity is compatible with, enables, or is configured to work under a specified license.
-
D.
licenseFamily
Indicates that one license belongs to, is derived from, or is categorized under a broader family or class of related licenses.
-
E.
licenseFor
Indicates that one entity grants or holds formal permission or authorization for another entity to perform an activity, use a resource, or operate under specified conditions.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6c3ace4819081bc2b86ee2486b6 |
completed | March 1, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69a4c48902808190a8028d359bcf123e |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:12 p.m.