Triple
T16271610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wind Point Lighthouse |
E395011
|
entity |
| Predicate | hasKeeperQuartersUse |
P122457
|
FINISHED |
| Object | municipal offices and event space |
—
|
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: municipal offices and event space | Statement: [Wind Point Lighthouse, hasKeeperQuartersUse, municipal offices and event space]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKeeperQuartersUse Context triple: [Wind Point Lighthouse, hasKeeperQuartersUse, municipal offices and event space]
-
A.
hasKeeperHouse
Indicates that an entity is associated with or assigned to a specific keeper’s house or residence.
-
B.
hasCastleUse
Indicates that something is used or designated for a particular castle-related purpose or function.
-
C.
hasFloorUse
Indicates that a particular floor or level of a building is designated for a specific function, activity, or type of use.
-
D.
hasClerk
Indicates that an entity is served, assisted, or managed by a clerk associated with it.
-
E.
hasAuctionHouse
Indicates that one entity is associated with, operated by, or conducted through a particular auction house.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2460a4f7c8190a614c11f7eaa0a7a |
completed | April 17, 2026, 2:39 p.m. |
| PD | Predicate disambiguation | batch_69e219f68d308190b71c1601303f0628 |
completed | April 17, 2026, 11:31 a.m. |
| PDg | Predicate description generation | batch_69e21e56e0348190a3d9475360231a70 |
completed | April 17, 2026, 11:49 a.m. |
Created at: April 10, 2026, 5:05 a.m.