Triple
T30559234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Mojarra Stela 1 |
E777788
|
entity |
| Predicate | hasLongCountDate |
P193127
|
FINISHED |
| Object | early Long Count date |
—
|
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: early Long Count date | Statement: [La Mojarra Stela 1, hasLongCountDate, early Long Count date]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLongCountDate Context triple: [La Mojarra Stela 1, hasLongCountDate, early Long Count date]
-
A.
hasLong
Indicates that an entity possesses or exhibits a great extent or duration in some measurable dimension (such as length or time).
-
B.
hasDayCount
Indicates that an entity is associated with a specific number of days, expressing the duration or count of days related to it.
-
C.
hasMonthCount
Indicates a relationship where an entity is associated with a specific number of months.
-
D.
hasLongTermDatasetSince
Indicates that an entity has maintained or used a particular dataset continuously starting from a specified point in time.
-
E.
hasCountingPeriod
Indicates that there is a defined time span or interval over which occurrences, quantities, or measurements related to an entity are counted or aggregated.
- 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_69f2249ed41c8190b175170ecfd6e1c5 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd389cb28c819099a77e28d25f258a |
completed | May 8, 2026, 1:13 a.m. |
| PD | Predicate disambiguation | batch_69fd3826d8048190ada79a5868d1d7f3 |
completed | May 8, 2026, 1:11 a.m. |
| PDg | Predicate description generation | batch_69fd389b653c81908a97ab2eff98c6ea |
completed | May 8, 2026, 1:12 a.m. |
Created at: April 29, 2026, 8:21 p.m.