Triple
T7698075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Recorded Texas Historic Landmark |
E174417
|
entity |
| Predicate | markerColor |
P78346
|
FINISHED |
| Object | silver text on dark background |
—
|
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: silver text on dark background | Statement: [Recorded Texas Historic Landmark, markerColor, silver text on dark background]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: markerColor Context triple: [Recorded Texas Historic Landmark, markerColor, silver text on dark background]
-
A.
maskColor
Indicates the color attribute associated with a mask.
-
B.
anchorColor
Indicates the color attribute assigned to an anchor or anchoring element in the relationship.
-
C.
mapColor
Indicates a relationship where a map region or area is assigned or associated with a specific color, typically for visualization or categorization purposes.
-
D.
capeColor
Indicates the color attribute associated with a cape worn or possessed by an entity.
-
E.
trackColor
Indicates the color associated with a given track in a context such as audio, video, or data sequencing.
- 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_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. |
| PDg | Predicate description generation | batch_69c7040091608190a9e46ecfb2ff0bca |
completed | March 27, 2026, 10:26 p.m. |
Created at: March 27, 2026, 4:03 p.m.