Triple
T27148495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Recorded Texas Historic Landmark |
E682317
|
entity |
| Predicate | markerInscription |
P51076
|
FINISHED |
| Object | RECORDED TEXAS HISTORIC LANDMARK |
—
|
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: RECORDED TEXAS HISTORIC LANDMARK | Statement: [Recorded Texas Historic Landmark, markerInscription, RECORDED TEXAS HISTORIC LANDMARK]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: markerInscription Context triple: [Recorded Texas Historic Landmark, markerInscription, RECORDED TEXAS HISTORIC LANDMARK]
-
A.
materialTypicallyInscribedOn
Indicates the material that is most commonly used as the surface or medium on which something is inscribed.
-
B.
mentionedInInscription
chosen
Indicates that an entity is referenced or named within a specific inscription or inscribed text.
-
C.
notableInscriptionBy
Indicates that an inscription of particular note or significance on an entity was created, authored, or carved by a specified agent.
-
D.
marksOn
Indicates that one entity bears visible signs, traces, or imprints that have been made or left by another entity.
-
E.
marque
Indicates that one entity is the brand or make associated with another entity, such as a product, vehicle, or manufactured item.
- 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_69eefaceb2a08190b9659b7f730629f5 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f643ed0b7481908cf25f3afec0a61d |
completed | May 2, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69f641dc8ff48190ab575d855616580c |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 9:12 a.m.