Triple
T30483492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MacBook Air |
E775650
|
entity |
| Predicate | firstRetinaModel |
P203897
|
FINISHED |
| Object | MacBook Air (Retina, 13-inch, 2018) |
E772903
|
NE 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: MacBook Air (Retina, 13-inch, 2018) | Statement: [MacBook Air, firstRetinaModel, MacBook Air (Retina, 13-inch, 2018)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstRetinaModel Context triple: [MacBook Air, firstRetinaModel, MacBook Air (Retina, 13-inch, 2018)]
-
A.
firstModel
Indicates that an entity is the initial or earliest model/version in a sequence or series of models.
-
B.
firstFlagshipModel
Indicates that the subject is the earliest or initial flagship model associated with the object, typically marking the first leading or premier product in a series or lineup.
-
C.
firstModelLineOf
Indicates that one entity is the first line of a model or modeling construct associated with another entity.
-
D.
firstModelToFly
Indicates that the subject is the earliest or original model among a set that successfully achieved flight.
-
E.
firstCameraBody
Indicates that one camera body is the initial or primary camera body associated with a given photographer, setup, or camera system.
- F. None of above. chosen
Provenance (5 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_69f22497f91c8190afa7165bc900accd |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a0248062e608190aae1afdae38d2e5a |
completed | May 11, 2026, 9:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a27ac2821988190b00de2a44a5e0083 |
completed | June 9, 2026, 6:01 a.m. |
| PD | Predicate disambiguation | batch_6a02465a0d38819084c3a6813fb943fc |
completed | May 11, 2026, 9:12 p.m. |
| PDg | Predicate description generation | batch_6a02480580b481909838e544d0ebfd17 |
completed | May 11, 2026, 9:20 p.m. |
Created at: April 29, 2026, 8:13 p.m.