Triple
T7698081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Recorded Texas Historic Landmark |
E174417
|
entity |
| Predicate | oftenLocatedOn |
P18608
|
FINISHED |
| Object | building façade or near main entrance |
—
|
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: building façade or near main entrance | Statement: [Recorded Texas Historic Landmark, oftenLocatedOn, building façade or near main entrance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenLocatedOn Context triple: [Recorded Texas Historic Landmark, oftenLocatedOn, building façade or near main entrance]
-
A.
isLocatedOn
chosen
Indicates that one entity exists at or is situated upon the surface or area of another entity.
-
B.
partlyLocatedOn
Indicates that one entity is situated such that only a portion of it lies on or within the spatial extent of another entity.
-
C.
sometimesLocatedIn
Indicates that an entity is located in a given place only at certain times or under certain conditions, rather than permanently or always.
-
D.
livesOn
Indicates that one entity resides or has its home on or atop another entity (such as a surface, structure, or geographic feature).
-
E.
locatedAlong
Indicates that one entity is situated adjacent to, or running beside, the length or course of another linear feature (such as a road, river, or railway).
- 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_69c6995a72cc8190998e56daa6f8e453 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70402169481909b219dc5f4a64b9b |
completed | March 27, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69c70165e78c8190bf6b3c34e243cb81 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:03 p.m.