Triple
T3723417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Lorenzo Tenochtitlán |
E81691
|
entity |
| Predicate | materialOfMonuments |
P1272
|
FINISHED |
| Object | basalt |
—
|
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: basalt | Statement: [San Lorenzo Tenochtitlán, materialOfMonuments, basalt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: materialOfMonuments Context triple: [San Lorenzo Tenochtitlán, materialOfMonuments, basalt]
-
A.
monumentType
Indicates the specific kind or category of monument that an entity is classified as.
-
B.
materialDepicted
Indicates that a work or representation visually portrays or includes a particular material as part of its subject.
-
C.
materialUsed
chosen
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
D.
monumentSubject
Indicates that the subject serves as the monument or commemorative structure associated with another entity.
-
E.
builtMonument
Indicates that one entity constructed or created a monument in honor of, or related to, another entity.
- 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_69ad8b1b7ef081908d2d381bbf54985a |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcaf3f2ec8190a3a8f363cfb762f3 |
completed | March 8, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69adc0436e508190909ec4a3e8443aef |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:34 p.m.