Triple
T8710866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pagumen |
E206772
|
entity |
| Predicate | hasPositionInYear |
P46420
|
FINISHED |
| Object | thirteenth month |
—
|
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: thirteenth month | Statement: [Pagumen, hasPositionInYear, thirteenth month]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPositionInYear Context triple: [Pagumen, hasPositionInYear, thirteenth month]
-
A.
hasNumberInYear
Indicates that a specific number is associated with or occurs within a given year.
-
B.
positionInCivilYear
chosen
Indicates the specific point or ordering of something within the sequence of time units that make up a civil (calendar) year.
-
C.
hasAssociatedYear
Indicates that an entity is linked to a specific year that is relevant to it (e.g., creation, occurrence, or reference year).
-
D.
yearPassed
Indicates that a specified number of calendar years has elapsed between two time points or events.
-
E.
hasLetterNumberInYear
Indicates that a specific letter or document is assigned a particular sequential number within a given year.
- 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_69ca835645e881908f00e3c8b51da81d |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5c3189f88190bb9bb77ba9d28d60 |
completed | March 31, 2026, 11:43 p.m. |
| PD | Predicate disambiguation | batch_69cc456e806c819087e7d66ee737f242 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:35 p.m.