Triple
T20983339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | We'll Make It |
E516823
|
entity |
| Predicate | maxEquippedPerCharacter |
P32169
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [We'll Make It, maxEquippedPerCharacter, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxEquippedPerCharacter Context triple: [We'll Make It, maxEquippedPerCharacter, 1]
-
A.
oftenEquippedWith
Indicates that one type of entity is frequently or typically outfitted, furnished, or supplied with another entity.
-
B.
isHeavilyEquipped
Indicates that an entity is carrying or outfitted with a large amount of equipment, gear, or armament.
-
C.
numberOfPlayableCharacters
Indicates the total count of distinct characters that can be actively controlled or played by a user in a game or interactive experience.
-
D.
maximumDevicesPerChannel
chosen
Indicates the highest number of devices that are allowed to be associated with or operate on a single channel.
-
E.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
- 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_69e0b4ffac148190bbade9f0eceb660b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fbe03244819097630333e70c4e88 |
completed | April 21, 2026, 4:24 a.m. |
| PD | Predicate disambiguation | batch_69e5dbe6976081908abd4e9c8734bae9 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 1:48 p.m.