Triple
T11453129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Steuben |
E271452
|
entity |
| Predicate | hasPresentUse |
P99647
|
FINISHED |
| Object | museum |
—
|
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: museum | Statement: [Fort Steuben, hasPresentUse, museum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPresentUse Context triple: [Fort Steuben, hasPresentUse, museum]
-
A.
hasFormerUse
Indicates that something previously served a particular function or role that it no longer has.
-
B.
hasHumanUse
Indicates that something is used, employed, or utilized by humans for a particular purpose or benefit.
-
C.
hasUsePattern
Indicates a characteristic or recurring way in which something is typically used or applied.
-
D.
hasUseCase
Indicates that one entity is employed, applied, or utilized as a solution or method to address a particular need, problem, or scenario associated with another entity.
-
E.
hasHumanPresence
Indicates that humans are physically present in or occupying a given location, object, or context.
- F. None of above. chosen
Provenance (4 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f2138081909408c7916cef99c9 |
completed | April 9, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69d80867ff248190bb157fa9e355353b |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:35 p.m.