Triple
T140289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gettysburg Address |
E2835
|
entity |
| Predicate | lengthInWords |
P6006
|
FINISHED |
| Object | about 272 words |
—
|
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: about 272 words | Statement: [Gettysburg Address, lengthInWords, about 272 words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lengthInWords Context triple: [Gettysburg Address, lengthInWords, about 272 words]
-
A.
length
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
B.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
-
C.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
-
D.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
E.
hasNumberOfVowelLetters
Indicates that an entity is associated with a specific count of vowel letters it contains.
- 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_69a2521e35c08190b28e5c9f1e3c9b59 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257c679d88190bc71775dab2cfc64 |
completed | Feb. 28, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69a2565426c08190aab68e34a6a2d60e |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a25737f9188190b9690dce98aed83a |
completed | Feb. 28, 2026, 2:47 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.